1
|
Shen J, Liu Q, Feng X. Hourly PM 2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122703. [PMID: 39357440 DOI: 10.1016/j.jenvman.2024.122703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 09/22/2024] [Accepted: 09/27/2024] [Indexed: 10/04/2024]
Abstract
Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %-20.00 %, reduced root mean square error (RMSE) by 13.22 %-17.11 %, improved coefficient of determination (R2) by 10 %-35.38 %, and improved accuracy (ACC) by 3.48 %-7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.
Collapse
Affiliation(s)
- Jinxing Shen
- College of Civil and Transportation Engineering, Hohai University, No.1, Xikang Road, Nanjing, 210098, China.
| | - Qinxin Liu
- College of Civil and Transportation Engineering, Hohai University, No.1, Xikang Road, Nanjing, 210098, China
| | - Xuejun Feng
- College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing, 210098, China
| |
Collapse
|
2
|
Han D, Shi L, Wang M, Zhang T, Zhang X, Li B, Liu J, Tan Y. Variation pattern, influential factors, and prediction models of PM2.5 concentrations in typical urban functional zones of northeast China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 954:176299. [PMID: 39284444 DOI: 10.1016/j.scitotenv.2024.176299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 09/01/2024] [Accepted: 09/13/2024] [Indexed: 10/01/2024]
Abstract
This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.
Collapse
Affiliation(s)
- Dongliang Han
- School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, China.
| | - Luyang Shi
- College of National Defence Engineering, Army Engineering University of PLA, Nanjing, China.
| | - Mingqi Wang
- Department of Architecture, National University of Singapore, Singapore
| | - Tiantian Zhang
- School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, China.
| | - Xuedan Zhang
- College of Civil Engineering, Northeast Forestry University, Harbin 150040, China
| | - Baochang Li
- Heilongjiang Institute of Construction Technology, Harbin, China
| | - Jing Liu
- School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, China
| | - Yufei Tan
- School of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, China
| |
Collapse
|
3
|
Zhou S, Wang W, Zhu L, Qiao Q, Kang Y. Deep-learning architecture for PM 2.5 concentration prediction: A review. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 21:100400. [PMID: 38439920 PMCID: PMC10910069 DOI: 10.1016/j.ese.2024.100400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/06/2024]
Abstract
Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.
Collapse
Affiliation(s)
- Shiyun Zhou
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Wang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Long Zhu
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Qi Qiao
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yulin Kang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| |
Collapse
|
4
|
Haghbayan S, Momeni M, Tashayo B. A new attention-based CNN_GRU model for spatial-temporal PM 2.5 prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:53140-53155. [PMID: 39174828 DOI: 10.1007/s11356-024-34690-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/08/2024] [Indexed: 08/24/2024]
Abstract
Accurately predicting the spatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.
Collapse
Affiliation(s)
- Sara Haghbayan
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran
| | - Mehdi Momeni
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.
| | - Behnam Tashayo
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran
- Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran
| |
Collapse
|
5
|
Liu Z, Ji D, Wang L. PM 2.5 concentration prediction based on EEMD-ALSTM. Sci Rep 2024; 14:12636. [PMID: 38825660 PMCID: PMC11144699 DOI: 10.1038/s41598-024-63620-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024] Open
Abstract
The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.
Collapse
Affiliation(s)
- Zuhan Liu
- School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
| | - Dong Ji
- School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
| | - Lili Wang
- College of Science, Nanchang Institute of Technology, Nanchang, 330099, China
| |
Collapse
|
6
|
Wang W, Liu B, Tian Q, Xu X, Peng Y, Peng S. Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 350:124053. [PMID: 38677458 DOI: 10.1016/j.envpol.2024.124053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 04/29/2024]
Abstract
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.
Collapse
Affiliation(s)
- Wenyuan Wang
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Bochi Liu
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Qi Tian
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Xinglu Xu
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Yun Peng
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Shitao Peng
- Tianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin, 300456, China.
| |
Collapse
|
7
|
Wood DA. Trend-attribute forecasting of hourly PM2.5 trends in fifteen cities of Central England applying optimized machine learning feature selection. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120561. [PMID: 38479290 DOI: 10.1016/j.jenvman.2024.120561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/18/2024] [Accepted: 03/05/2024] [Indexed: 04/07/2024]
Abstract
Recorded particulate matter (PM2.5) hourly trends are compared for fifteen urban recording sites distributed across central England for the period 2018 to 2022. They include 10 urban-background and five urban-traffic (roadside) sites with some located within the same urban area. The sites all show consistent background and peak distributions with mean annual values and standard deviations higher for 2018 and 2019 than for 2020 to 2022. The objective of this study is to demonstrate that trend attributes extracted from hourly recorded univariate PM2.5 trends at these sites can be used to provide reliable short-term hourly predictions and provide valuable insight into the regional variations in the recorded trends. Fifteen trend attributes extracted from the prior 12 h (t-1 to t-12) of recorded PM2.5 data were compiled and used as input to four supervised machine learning models (SML) to forecast PM2.5 concentrations up to 13 h ahead (t0 to t+12). All recording sites delivered forecasts with similar ranges of error levels for specific hours ahead which are consistent with their PM2.5 recorded ranges. Forecasting results for four representative sites are presented in detail using models trained and cross-validated with 2020 and 2021 hourly data to forecast 2021 and 2022 hourly data, respectively. A novel optimized feature selection procedure using a suite of five optimizers is used to improve the efficiency of the forecasting models. The LASSO and support vector regression models generate the best and most generalizable hourly PM2.5 forecasts from trained and validated SML models with mean average error (MAE) of between ∼1 and ∼3 μg/m3 for t0 to t+3 h ahead. A novel overfitting indicator, exploiting the cross-validation mean values, demonstrates that these two models are not affected by overfitting. Forecasts for t+6 to t+12 h forward generate higher MAE values between ∼3 and ∼4 μg/m3 due to their tendency to underestimate some of the extreme PM2.5 peaks. These findings indicate that further model refinements are required to generate more reliable short-term predictions for the t+6 to t+24 h ahead.
Collapse
|
8
|
Gholami H, Mohammadifar A, Behrooz RD, Kaskaoutis DG, Li Y, Song Y. Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:123082. [PMID: 38061429 DOI: 10.1016/j.envpol.2023.123082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/11/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m-3 and 73 μg m-3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.
Collapse
Affiliation(s)
- Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
| | - Aliakbar Mohammadifar
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
| | - Reza Dahmardeh Behrooz
- Department of Environmental Science, Faculty of Natural Resources, University of Zabol, P.O. Box 98615-538, Zabol, Iran
| | - Dimitris G Kaskaoutis
- Department of Chemical Engineering, University of Western Macedonia, Kozani, 50100, Greece
| | - Yue Li
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; Laoshan Laboratory, Qingdao, 266061, China
| | - Yougui Song
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; Laoshan Laboratory, Qingdao, 266061, China.
| |
Collapse
|
9
|
Yuan P, Jiao Y, Li J, Xia Y. A densely connected causal convolutional network separating past and future data for filling missing PM 2.5 time series data. Heliyon 2024; 10:e24738. [PMID: 38312707 PMCID: PMC10835227 DOI: 10.1016/j.heliyon.2024.e24738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/07/2024] [Accepted: 01/12/2024] [Indexed: 02/06/2024] Open
Abstract
Air pollution poses a significant threat to human health and the environment globally. Precise analysis and prediction of pollutant concentrations are essential for monitoring and managing air quality. However, reliable analysis and prediction require comprehensive and high-quality data, which is often compromised due to missing data during collection. Unfortunately, conventional methods for addressing missing data fall short of providing adequate solutions. The missing data for air quality indicators are commonly systematic, with all data points missing for extended periods. This makes it difficult to establish correlations and populate the missing data accurately. To address this problem, we propose a Densely Connected Causal Convolutional Network Separating Past and Future Data (DCCN-SPF), a deep learning-based model that fills in continuous missing PM2.5 concentration data in the original dataset. It extracts features from past and future data separately using densely connected causal convolutional networks and incorporates linear interpolation and deep learning structures to improve prediction accuracy. Using air quality monitoring data from the China Environmental Monitoring Station between 2017 and 2021 in Beijing, we compare our proposed model with baseline models and find that our model outperforms others in predicting PM2.5 concentrations. The evaluation metrics MAE and RMSE are used, revealing significant reductions of 8.7-21.6 % for MAE and 7.1-23.5 % for RMSE in favor of our proposed DCCN-SPF model.
Collapse
Affiliation(s)
- Peng Yuan
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Yiwen Jiao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Jiaxue Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Yan Xia
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China
| |
Collapse
|
10
|
Shi T, Li P, Yang W, Qi A, Qiao J. Application of TCN-biGRU neural network in [Formula: see text] concentration prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:119506-119517. [PMID: 37930575 DOI: 10.1007/s11356-023-30354-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023]
Abstract
Fine particulate matter ([Formula: see text]) poses a significant threat to human life and health, and therefore, accurately predicting [Formula: see text] concentration is critical for controlling air pollution. Two improved types of recurrent neural networks (RNNs), the long short-term memory (LSTM) and gated recurrent unit (GRU), have been widely used in time series data prediction due to their ability to capture temporal features. However, both degrade into random guessing as the time length increases. In order to enhance the accuracy of [Formula: see text] concentration prediction and address the issue of random guessing in RNNs neural networks, this study introduces a TCN-biGRU neural network model. This model is a hybrid prediction approach based on combining temporal convolutional networks (TCN) and bidirectional gated recurrent units (bi-GRU). TCN extracts higher-level feature information from longer time series data of [Formula: see text] concentrations, while bi-GRU captures features from past and future data to achieve more accurate predictive outcomes. This case study utilizes data from monitoring stations in Beijing in 2021 for conducting [Formula: see text] prediction experiments. The TCN-biGRU model achieves an average absolute error, root mean square error, and [Formula: see text] of 4.20, 7.71, and 0.961 in its predictive outcomes. When compared to the predictive outcomes of individual LSTM, GRU, and bi-GRU models, it is evident that the TCN-biGRU model exhibits smaller errors and superior predictive performance.
Collapse
Affiliation(s)
- Ting Shi
- Faculty of Information Technology, Beijing University of Technology, Nanmofang, 100124, Beijing, China.
| | - Pengyu Li
- Faculty of Information Technology, Beijing University of Technology, Nanmofang, 100124, Beijing, China
| | - Wu Yang
- Faculty of Information Technology, Beijing University of Technology, Nanmofang, 100124, Beijing, China
| | - Ailin Qi
- Faculty of Information Technology, Beijing University of Technology, Nanmofang, 100124, Beijing, China
| | - Junfei Qiao
- Faculty of Information Technology, Beijing University of Technology, Nanmofang, 100124, Beijing, China
| |
Collapse
|
11
|
Wang Z, Wu X, Wu Y. A spatiotemporal XGBoost model for PM 2.5 concentration prediction and its application in Shanghai. Heliyon 2023; 9:e22569. [PMID: 38058450 PMCID: PMC10696222 DOI: 10.1016/j.heliyon.2023.e22569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023] Open
Abstract
This paper innovatively constructed an analytical and forecasting framework to predict PM2.5 concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, improvements are made on XGBoost prediction accuracy and stability so that prediction deviation at extreme points can be avoided. The main findings of this paper can be summarized as follows: 1) Compared with the original model, the goodness of fit of the modified XGBoost model on the test set increased by 17 %, and the root mean square error decreased by 28 %; 2) The variation of PM2.5 concentration in Shanghai has a significant seasonal (cyclical) effect, and its fluctuation period is 3 months (a quarter). In winter, the frequency of extreme value points is significantly higher than that in other seasons; 3) In terms of spatial distribution, the PM2.5 concentration in the central city of Shanghai is higher than that in the rural areas, and the PM2.5 concentration gradually decreases from center city to the surrounding areas. The innovation and contribution of this paper can be summarized as follows: 1) EEMD algorithm verified by SSA was used to decompose the original model without reconstructing all subsequences and get the best weighing among each part of the hybrid model by using variable weight assignment; 2) The city was cut into pieces according to administrative districts in avoid of the duplicate analysis when utilizing advised Kriging interpolation; 3) IDW method was applied to verified Kriging interpolation to increase the accuracy; 4) The latitude and longitude were innovatively converted into the arc length of the corresponding spherical surface; 5) Hierarchical analysis method was used to obtain the order of importance among the PM2.5 monitoring stations, which could improve the accuracy and achieve dimension reduction.
Collapse
Affiliation(s)
- Zidong Wang
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
| | - Xianhua Wu
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
| | - You Wu
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
| |
Collapse
|
12
|
Guo Q, He Z, Wang Z. Simulating daily PM 2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data. CHEMOSPHERE 2023; 340:139886. [PMID: 37611770 DOI: 10.1016/j.chemosphere.2023.139886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/18/2023] [Indexed: 08/25/2023]
Abstract
Accurate PM2.5 concentrations predicting is critical for public health and wellness as well as pollution control. However, traditional methods are difficult to accurately predict PM2.5. An adaptive model coupled with artificial neural network (ANN) and wavelet analysis (WANN) is utilized to predict daily PM2.5 concentrations with remote sensing and surface observation data. The four evaluation metrics, namely Pearson correlation coefficient (R), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), are utilized to evaluate the performances of the artificial neural network (ANN) and WANN methods. From the predicting results, The WANN model has a higher R (R = 0.9990) during the testing period compared with R (R = 0.6844) based on the ANN model. Similarly, the WANN model has a lower MAPE (3.6988%), RMSE (1.0145 μg/m3), MAE (1.3864 μg/m3), compared with MAPE (80.0086%), RMSE (16.5838 μg/m3), MAE (12.2420 μg/m3) of the ANN. In addition, comparing the outcomes of the proposed WANN method with the ANN method, it was observed that the error during the training and verification period has decreased significantly. Furthermore, the statistical methods are used to analyze WANN and ANN, showing that WANN has higher training accuracy and better stability. Therefore, it is feasible to establish WANN to predict PM2.5 concentrations (1 day in advance) by using remote sensing and surface observation data.
Collapse
Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China; Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing, 100081, China; State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China.
| | - Zhenfang He
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
| | - Zhaosheng Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| |
Collapse
|
13
|
Li Y, Li R. A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2023; 176:673-684. [PMID: 37350802 PMCID: PMC10264166 DOI: 10.1016/j.psep.2023.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 05/22/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023]
Abstract
Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.
Collapse
Affiliation(s)
- Yuting Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
| | - Ruying Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
| |
Collapse
|
14
|
Li Y, Sha Z, Tang A, Goulding K, Liu X. The application of machine learning to air pollution research: A bibliometric analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 257:114911. [PMID: 37154080 DOI: 10.1016/j.ecoenv.2023.114911] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/10/2023]
Abstract
Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.
Collapse
Affiliation(s)
- Yunzhe Li
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Zhipeng Sha
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Aohan Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Xuejun Liu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| |
Collapse
|
15
|
Teng M, Li S, Xing J, Fan C, Yang J, Wang S, Song G, Ding Y, Dong J, Wang S. 72-hour real-time forecasting of ambient PM 2.5 by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information. ENVIRONMENT INTERNATIONAL 2023; 176:107971. [PMID: 37220671 DOI: 10.1016/j.envint.2023.107971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/05/2023] [Accepted: 05/08/2023] [Indexed: 05/25/2023]
Abstract
The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R2 increases from 0.6 to 0.79), particularly for polluted episodes (PM2.5 concentration > 55 µg/m3) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM2.5 over the sites where the AOD can inform additional aloft PM2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM2.5 forecast is demonstrated by the increased performance in predicting PM2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.
Collapse
Affiliation(s)
- Mengfan Teng
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Siwei Li
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China.
| | - Jia Xing
- Department of Civil and Environmental Engineering, the University of Tennessee, Knoxville, TN 37996, USA
| | - Chunying Fan
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Jie Yang
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China
| | - Shuo Wang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Ge Song
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Yu Ding
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Jiaxin Dong
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Shansi Wang
- Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| |
Collapse
|
16
|
Tariq S, Mariam A, Mehmood U, Ul-Haq Z. Long term spatiotemporal trends and health risk assessment of remotely sensed PM 2.5 concentrations in Nigeria. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 324:121382. [PMID: 36863437 DOI: 10.1016/j.envpol.2023.121382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/11/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
PM2.5 is an important indicator reflecting air quality variations. Currently, environmental pollution related issues have become more severe that significantly threaten human health. The current study is an attempt to analyze the spatio-dynamic characteristics of PM2.5 in Nigeria based on the directional distribution and trend clustering analysis from 2001 to 2019. The results indicated that PM2.5 concentration increased in most of the Nigerian states, particularly in mid-northern and southern states. The lowest PM2.5 concentration in Nigeria is even beyond the interim target-1 (35 μg/m3) of the WHO. During the study period, the average PM2.5 concentration increased at a growth rate of 0.2 μg/m3/yr from 69 μg/m3 to 81 μg/m3. The growth rate varied from region to region. Kano, Jigawa, Katsina, Bauchi, Yobe, and Zamfara experienced the fastest growth rate of 0.9 μg/m3/yr with 77.9 μg/m3 mean concentration. The median center of the national average PM2.5 moved toward the north indicating the highest PM2.5 concentration in northern states. The Saharan desert dust is the dominant source of PM2.5 in northern areas. Moreover, agricultural practices and deforestation activities along with low rainfall increase desertification and air pollution in these regions. Health risks increased in most of the mid-northern and southern states. The extent of ultra-high health risk (UHR) areas corresponding to the 8×104-7.3×106 μg⋅person/m3 increased from 1.5% to 2.8%. Mainly Kano, Lagos, Oyo, Edo, Osun, Ekiti, southeastern Kwara, Kogi, Enugu, Anambra, Northeastern Imo, Abia, River, Delta, northeastern Bayelsa, Akwa Ibom, Ebonyi, Abuja, Northern Kaduna, Katsina, Jigawa, central Sokoto, northeastern Zamfara, central Borno, central Adamawa, and northwestern Plateau are under UHR areas.
Collapse
Affiliation(s)
- Salman Tariq
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan; Department of Space Science, University of the Punjab, Lahore, Pakistan
| | - Ayesha Mariam
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
| | - Usman Mehmood
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan; University of Management and Technology, Lahore, Pakistan
| | - Zia Ul-Haq
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan; Department of Space Science, University of the Punjab, Lahore, Pakistan
| |
Collapse
|
17
|
Ding H, Niu X, Zhang D, Lv M, Zhang Y, Lin Z, Fu M. Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:63036-63051. [PMID: 36952164 DOI: 10.1007/s11356-023-26209-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/26/2023] [Indexed: 05/10/2023]
Abstract
Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.
Collapse
Affiliation(s)
- HaoNan Ding
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China
| | - Xiaojun Niu
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China.
- Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming, 525000, People's Republic of China.
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, Guangzhou HigherEducation Mega Centre, South China University of Technology, Guangzhou, 510006, People's Republic of China.
- The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou, 510006, People's Republic of China.
| | - Dongqing Zhang
- Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming, 525000, People's Republic of China
| | - Mengyu Lv
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China
| | - Yang Zhang
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China
| | - Zhang Lin
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China
| | - Mingli Fu
- School of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, 382 Waihuan East Road, Guangzhou, 510006, People's Republic of China
| |
Collapse
|
18
|
Zhang X, Chen X, Zheng G, Cao G. Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition. ENVIRONMENTAL RESEARCH 2023; 221:115259. [PMID: 36634894 DOI: 10.1016/j.envres.2023.115259] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 12/16/2022] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
The accurate and reliable prediction of chlorophyll-a (Chl-a) concentration is of great significance in reservoir environment management and pollution control. To improve the accuracy of Chl-a index prediction, a novel hybrid water quality prediction method was proposed for gated recurrent unit (GRU) neural network based on particle swarm algorithm optimized variational modal decomposition (PV-GRU). The results showed that the variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) in this study effectively reduced the non-smooth of water quality data. In addition, the GRU neural network reduced the risk of overfitting the deep-learning model with small sample data. Overall, the PV-GRU prediction model exhibited significant superiority in predicting non-smooth and non-linear Chl-a sequences with a relatively small sample size. The prediction errors of PV-GRU model were all less than those of other comparative models, and the fitting determination coefficient R2 was 94.21%. These results indicated that the proposed PV-GRU model can effectively predict the content of Chl-a in reservoirs, which provides an alternative new method for water quality prediction to prevent and control eutrophication in reservoirs.
Collapse
Affiliation(s)
- Xihai Zhang
- School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China.
| | - Xianghui Chen
- School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China.
| | - Guochen Zheng
- Department of Ecology, Hebei University of Environmental Engineering, Qinhuangdao 066102, China.
| | - Guangli Cao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
| |
Collapse
|
19
|
Fei X, Lai Z, Fang Y, Ling Q. A dual attention-based fusion network for long- and short-term multivariate vehicle exhaust emission prediction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160490. [PMID: 36442627 DOI: 10.1016/j.scitotenv.2022.160490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 06/16/2023]
Abstract
The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.
Collapse
Affiliation(s)
- Xihong Fei
- University of Science and Technology of China, Hefei 230027, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
| | - Zefeng Lai
- University of Science and Technology of China, Hefei 230027, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
| | - Yi Fang
- University of Science and Technology of China, Hefei 230027, China.
| | - Qiang Ling
- University of Science and Technology of China, Hefei 230027, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
| |
Collapse
|
20
|
Guo Q, He Z, Wang Z. Predicting of Daily PM 2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China. TOXICS 2023; 11:51. [PMID: 36668777 PMCID: PMC9864912 DOI: 10.3390/toxics11010051] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.
Collapse
Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
| | - Zhenfang He
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Zhaosheng Wang
- Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| |
Collapse
|
21
|
Bhimavarapu U, Sreedevi M. An enhanced loss function in deep learning model to predict PM2.5 in India. INTELLIGENT DECISION TECHNOLOGIES 2022. [DOI: 10.3233/idt-220111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Fine particulate matter (PM2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models.
Collapse
Affiliation(s)
- Usharani Bhimavarapu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
| | - M. Sreedevi
- Department of CSE, Amrita Sai Institute of Science and Technology, Paritala, Andhra Pradesh, India
| |
Collapse
|
22
|
Dong L, Hua P, Gui D, Zhang J. Extraction of multi-scale features enhances the deep learning-based daily PM 2.5 forecasting in cities. CHEMOSPHERE 2022; 308:136252. [PMID: 36055593 DOI: 10.1016/j.chemosphere.2022.136252] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/14/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD); subsequently, a deep learning approach of long short-term memory (LSTM) was proposed. Five cities with unique meteorological and economic characteristics were selected to assess the predictive ability of the proposed model. The results revealed that PM2.5 pollution was generally more severe in inland cities (66.98 ± 0.76 μg m-3) than in coastal cities (40.46 ± 0.40 μg m-3). The modelling comparison showed that in each city, the secondary decomposition algorithm improved the accuracy and prediction stability of the prediction models. When compared with other prediction models, LSTM effectively extracted featured information and achieved relatively accurate time-series prediction. The hybrid model of CEEMDAN-VMD-LSTM achieved a better prediction in the five cities (R2 = 0.9803 ± 0.01) compared with the benchmark models (R2 = 0.7537 ± 0.03). The results indicate that the proposed approach can identify the inherent correlations and patterns among complex datasets, particularly in time-series analysis.
Collapse
Affiliation(s)
- Liang Dong
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510535, China
| | - Pei Hua
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, 510006, Guangzhou, China; School of Environment, South China Normal University, University Town, 510006, Guangzhou, China
| | - Dongwei Gui
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Jin Zhang
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Yangtze Institute for Conservation and Development, Hohai University, Nanjing, 210098, China; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.
| |
Collapse
|
23
|
Jin H, Zhong R, Liu M, Ye C, Chen X. Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 323:116273. [PMID: 36261986 DOI: 10.1016/j.jenvman.2022.116273] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/29/2022] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection.
Collapse
Affiliation(s)
- Haoyu Jin
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Ruida Zhong
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Moyang Liu
- The Fenner School of Environment and Society, The Australian National University (ANU), Canberra, ACT, 0200, Australia
| | - Changxin Ye
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaohong Chen
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China.
| |
Collapse
|
24
|
Comparison of PM2.5 prediction performance of the three deep learning models: A case study of Seoul, Daejeon, and Busan. J IND ENG CHEM 2022. [DOI: 10.1016/j.jiec.2022.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
25
|
Jin H, Chen X, Zhong R, Liu M. Influence and prediction of PM2.5 through multiple environmental variables in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 849:157910. [PMID: 35944645 DOI: 10.1016/j.scitotenv.2022.157910] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Fine particulate matter (PM2.5) is an important indicator to measure the degree of air pollution. With the pursuit of sustainable development of China's economy and society, air pollution has been paid more and more attention. The spatial distribution of PM2.5 is affected by multiple factors. In this study, we selected Normalized Difference Vegetation Index (NDVI), precipitation, temperature, wind speed and elevation data to analyze the impact of each variable on PM2.5 in different regions of China. The results show that the high-value areas of PM2.5 were mainly concentrated in the North China Plain, the middle and lower reaches of the Yangtze River Plain, the Sichuan Basin, and the Tarim Basin. PM2.5 showed an upward trend in North China, Northeast China and Northwest China, while in most of South China, especially the Sichuan Basin, PM2.5 showed a downward trend. Therefore, the northern region of China needs to take measures to curb the growth of PM2.5. In Northwest China, wind speed and temperature had a greater impact on PM2.5. In North China, wind speed had a greater impact on PM2.5. In southern China, temperature and NDVI had a greater impact on PM2.5. The deep learning model can better simulate the spatial distribution of PM2.5 based on the selected variables. The clustering effect of single variable is better than multivariate spatial information clustering based on principal component analysis (PCA). It is difficult to explain which variable has the greatest impact on PCA clustering. This study can provide an important reference for PM2.5 prevention and control in different regions of China.
Collapse
Affiliation(s)
- Haoyu Jin
- School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou 510275, China
| | - Xiaohong Chen
- School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou 510275, China.
| | - Ruida Zhong
- School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou 510275, China
| | - Moyang Liu
- The Fenner School of Environment and Society, The Australian National University (ANU), Canberra, ACT 0200, Australia
| |
Collapse
|
26
|
Yang S, Wu H. A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:78265-78291. [PMID: 35689778 DOI: 10.1007/s11356-022-21318-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers.
Collapse
Affiliation(s)
- Shaomei Yang
- Department of Economic Management, North China Electric Power University, No. 689, Huadian Road, Baoding City, Hebei Province, China
| | - Haoyue Wu
- Department of Economic Management, North China Electric Power University, No. 689, Huadian Road, Baoding City, Hebei Province, China.
| |
Collapse
|
27
|
He M, Che J, Jiang Z, Zhao W, Wan B. A novel decomposition-denoising ANFIS model based on singular spectrum analysis and differential evolution algorithm for seasonal AQI forecasting. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, few studies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-Differential Evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance.
Collapse
Affiliation(s)
- Mingjun He
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Jinxing Che
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Zheyong Jiang
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Weihua Zhao
- School of Science, Nantong University, Nantong, Jiangsu, China
| | - Bingrong Wan
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
| |
Collapse
|
28
|
PM2.5 forecasting for an urban area based on deep learning and decomposition method. Sci Rep 2022; 12:17565. [PMID: 36266317 PMCID: PMC9584903 DOI: 10.1038/s41598-022-21769-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/30/2022] [Indexed: 01/13/2023] Open
Abstract
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
Collapse
|
29
|
Yang H, Zhao J, Li G. A new hybrid prediction model of PM 2.5 concentration based on secondary decomposition and optimized extreme learning machine. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:67214-67241. [PMID: 35524096 DOI: 10.1007/s11356-022-20375-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
As air pollution worsens, the prediction of PM2.5 concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM2.5 concentration accurately. PM2.5 concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R2 of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R2 of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.
Collapse
Affiliation(s)
- Hong Yang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
| | - Junlin Zhao
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China
| | - Guohui Li
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China
| |
Collapse
|
30
|
Lin S, Zhao J, Li J, Liu X, Zhang Y, Wang S, Mei Q, Chen Z, Gao Y. A Spatial-Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM 2.5 Concentration Prediction. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1125. [PMID: 36010788 PMCID: PMC9407057 DOI: 10.3390/e24081125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/29/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial-temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial-temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win-tie-loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.
Collapse
Affiliation(s)
- Shaofu Lin
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Junjie Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Xiliang Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yumin Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Shaohua Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Qiang Mei
- Navigation College, Jimei University, Xiamen 361021, China
| | - Zhuodong Chen
- China National Petroleum Corporation Auditing Service Center, Beijing 100028, China
| | - Yuyao Gao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| |
Collapse
|
31
|
An Empirical Analysis of Audio-Visual Teaching and Network Multi-Modal Learning Environment Theory for English Majors. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:5000501. [PMID: 35990551 PMCID: PMC9388312 DOI: 10.1155/2022/5000501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/11/2022] [Indexed: 11/18/2022]
Abstract
In order to further investigate the network multi-modal learning environment to promote students' ability to English audio-visual effects and improve the quality of teaching effect, an empirical analysis of audio-visual teaching and network multi-modal learning environment theory for English majors was proposed in the research. The 98 students were chosen as the experiment objects from two classes in the same school. They were divided into the experiment class and the control class. The students in the experiment class were taught according to the teaching model based on network multi-modal learning environment theory. SPSS19.0 software was used to analyze the scores before and after the experiment. After 3 months of experiment teaching, the average score of the post-test of the experiment class was 13.65 points, 2.23 points higher than 11.42 points before the test. The average score of the control class was 11.08 points, 0.1 points lower than 11.18 points before the test. It could be seen that the teaching method based on a network multi-modal learning environment was more conducive to improving the audio-visual performance and the teaching effectiveness of English majors.
Collapse
|
32
|
Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks. SUSTAINABILITY 2022. [DOI: 10.3390/su14159430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM2.5. However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM2.5/0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m3 for Beijing, 1.33, 3.38, and 4.60 μg/m3 for Guangzhou, 1.37, 4.19, and 6.35 μg/m3 for Xi’an, and 2.20, 7.75, and 10.07 μg/m3 for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM2.5 concentrations from 1 to 12 h post.
Collapse
|
33
|
Liang X, Zhang Q, Hong C, Niu W, Yang M. Do Internet Search Data Help Forecast Air Passenger Demand? Evidence From China’s Airports. Front Psychol 2022; 13:809954. [PMID: 35783700 PMCID: PMC9244845 DOI: 10.3389/fpsyg.2022.809954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.
Collapse
Affiliation(s)
- Xiaozhen Liang
- School of Management, Shanghai University, Shanghai, China
| | - Qing Zhang
- School of Management, Shanghai University, Shanghai, China
| | - Chenxi Hong
- School of Management, Shanghai University, Shanghai, China
| | - Weining Niu
- Business School, Beijing International Studies University, Beijing, China
| | - Mingge Yang
- School of Management, Shanghai University, Shanghai, China
- *Correspondence: Mingge Yang,
| |
Collapse
|
34
|
Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
35
|
Abstract
Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.
Collapse
|
36
|
Wang J, Xu W, Dong J, Zhang Y. Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:3417-3437. [PMID: 35369125 PMCID: PMC8956459 DOI: 10.1007/s00477-022-02202-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.
Collapse
Affiliation(s)
- Jujie Wang
- School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Wenjie Xu
- School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Jian Dong
- School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Yue Zhang
- School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| |
Collapse
|
37
|
Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation sensing. Firstly, on the basis of regional grid division, the inverse distance spatial interpolation method is used for spatial interpolation to collect regional VOCs data information. Secondly, extreme gradient boosting (XGBoost) is used for spatio-temporal feature selection, combined with graph convolutional neural network (GCN) to construct regional spatial relationships of VOCs, and multiple linear regression (MLR) to process VOCs time series data and predict the VOCs concentration in the grid. Finally, the aggregation potential values of VOCs are calculated based on the prediction results, and the potential perception results are visualised. A VOCs aggregation perception method based on concentration prediction is proposed, using the XGBoost-GCN-MLR method with a scenario-aware approach for VOCs to perceive the VOCs aggregation in the relevant region. VOCs concentration prediction and VOCs aggregation trend perception were carried out in Xi’an, Baoji, Tongchuan, Weinan and Xianyang. The results show that compared with the GCN model, XGBoost model, MLR model and GCN-MLR model, the XGBoost-GCN-MLR model reduces the input variables, achieves the optimisation of the input parameters of the VOCs concentration prediction model, reduces the complexity of the prediction model and improves the prediction accuracy. Intelligent sensing of VOCs aggregation can visualise the regional VOCs. The intelligent sensing of VOCs aggregation can visualise the development trend and status of regional VOCs aggregation and convey more information, which has practical value.
Collapse
|
38
|
Zhou Q, Zhou C, Wang X. Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection. PLoS One 2022; 17:e0262501. [PMID: 35120138 PMCID: PMC8815979 DOI: 10.1371/journal.pone.0262501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/26/2021] [Indexed: 12/04/2022] Open
Abstract
With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, we propose a hybrid stock forecasting model based on Feature Selection, Convolutional Neural Network and Bidirectional Gated Recurrent Unit (FS-CNN-BGRU). Feature Selection (FS) can select the data with better performance for the results as the input data after data normalization. Convolutional Neural Network (CNN) is responsible for feature extraction. It can extract the local features of the data, pay attention to more local information, and reduce the amount of calculation. The Bidirectional Gated Recurrent Unit (BGRU) can process the data with time series, so that it can have better performance for the data with time series attributes. In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). The results show that the performance of the hybrid model (FS-CNN-BGRU) is better than other single models, which has a certain reference value.
Collapse
Affiliation(s)
- Qihang Zhou
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Changjun Zhou
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Xiao Wang
- Xingzhi College Zhejiang Normal University, Jinhua, China
| |
Collapse
|
39
|
Zhang Y, Yao S, Yang R, Liu X, Qiu W, Han L, Zhou W, Shang W. Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:135-145. [PMID: 35030083 DOI: 10.1109/tnsre.2022.3143540] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.
Collapse
|
40
|
Zaini N, Ean LW, Ahmed AN, Malek MA. A systematic literature review of deep learning neural network for time series air quality forecasting. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:4958-4990. [PMID: 34807385 DOI: 10.1007/s11356-021-17442-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
Collapse
Affiliation(s)
- Nur'atiah Zaini
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Selangor, Malaysia.
| | - Lee Woen Ean
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Selangor, Malaysia
| | | |
Collapse
|
41
|
Cheng Y, He LY, Huang XF. Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 299:113670. [PMID: 34479147 DOI: 10.1016/j.jenvman.2021.113670] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/22/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R2 (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.
Collapse
Affiliation(s)
- Yong Cheng
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
| | - Ling-Yan He
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China
| | - Xiao-Feng Huang
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| |
Collapse
|
42
|
Jiang F, Zhang C, Sun S, Sun J. Forecasting hourly PM 2.5 based on deep temporal convolutional neural network and decomposition method. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107988] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|