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Guo Q, He Z, Wang Z. Monthly climate prediction using deep convolutional neural network and long short-term memory. Sci Rep 2024; 14:17748. [PMID: 39085577 PMCID: PMC11291741 DOI: 10.1038/s41598-024-68906-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024] Open
Abstract
Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951-31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.
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Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China.
- Institute of Huanghe Studies, 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
- Institute of Huanghe Studies, Liaocheng University, Liaocheng, 252000, 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
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2
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Mutlu A, Aydın Keskin G, Çıldır İ. Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:759. [PMID: 39046576 DOI: 10.1007/s10661-024-12908-4] [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: 02/13/2024] [Accepted: 07/11/2024] [Indexed: 07/25/2024]
Abstract
This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg-Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.
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Affiliation(s)
- Atilla Mutlu
- Department of Environmental Engineering, College of Engineering, Balikesir University, Balikesir, Turkey.
| | - Gülşen Aydın Keskin
- Department of Industrial Engineering, College of Engineering, Balikesir University, Balikesir, Turkey
| | - İhsan Çıldır
- Ministry of Health Edremit State Hospital, Edremit, Balikesir, Turkey
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3
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Wong PY, Su HJ, Candice Lung SC, Liu WY, Tseng HT, Adamkiewicz G, Wu CD. Explainable geospatial-artificial intelligence models for the estimation of PM 2.5 concentration variation during commuting rush hours in Taiwan. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 349:123974. [PMID: 38615837 DOI: 10.1016/j.envpol.2024.123974] [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: 10/24/2023] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.
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Affiliation(s)
- Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Huey-Jen Su
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
| | - Wan-Yu Liu
- Department of Forestry, National Chung Hsing University, Taichung, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan
| | - Hsiao-Ting Tseng
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Gary Adamkiewicz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Chih-Da Wu
- Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Mala S, Kukunuri A. An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network. NETWORK (BRISTOL, ENGLAND) 2024:1-34. [PMID: 38743436 DOI: 10.1080/0954898x.2024.2350578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/28/2024] [Indexed: 05/16/2024]
Abstract
Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the "Symmetric Convolution-based Residual Attention Network (SC-RAN)" is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.
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Affiliation(s)
- Sreedhar Mala
- ECE, Jawaharlal Nehru Technological University Anantapur, Anantapur, Andhra Pradesh, India
| | - Aparna Kukunuri
- ECE Department, JNTUA College of Engineering, Constituent college of Jawaharlal Nehru Technological University Anantapur, Anantapur, Andhra Pradesh, India
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5
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Moezzi SMM, Mohammadi M, Mohammadi M, Saloglu D, Sheikholeslami R. Machine learning insights into PM 2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:453. [PMID: 38619639 DOI: 10.1007/s10661-024-12567-5] [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: 12/23/2023] [Accepted: 03/23/2024] [Indexed: 04/16/2024]
Abstract
This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.
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Affiliation(s)
| | - Mitra Mohammadi
- Department of Environmental Science, Kheradgarayan Motahar Institute of Higher Education, Mashhad, Iran.
| | | | - Didem Saloglu
- Department of Disaster and Emergency Management, Disaster Management Institute, Istanbul Technical University, Istanbul, Turkey
| | - Razi Sheikholeslami
- Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
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6
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Lee YM, Lin GY, Le TC, Hong GH, Aggarwal SG, Yu JY, Tsai CJ. Characterization of spatial-temporal distribution and microenvironment source contribution of PM 2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques. ENVIRONMENTAL RESEARCH 2024; 244:117906. [PMID: 38101720 DOI: 10.1016/j.envres.2023.117906] [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: 07/03/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023]
Abstract
Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.
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Affiliation(s)
- Yi-Ming Lee
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Guan-Yu Lin
- Department of Environmental Science and Engineering, Tunghai University, Taichung, Taiwan.
| | - Thi-Cuc Le
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Gung-Hwa Hong
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shankar G Aggarwal
- Environmental Sciences & Biomedical Metrology Division, CSIR-National Physical Laboratory, New Delhi, India
| | - Jhih-Yuan Yu
- Division Chief, Department of Environmental Monitoring and Information Management, Environmental Protection Administration, Taiwan
| | - Chuen-Jinn Tsai
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
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7
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Xu X, Wei A, Tang S, Liu Q, Shi H, Sun W. Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:2167-2186. [PMID: 38055175 DOI: 10.1007/s11356-023-31250-9] [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: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023]
Abstract
Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.
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Affiliation(s)
- Xiaozhen Xu
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Anlei Wei
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China.
| | - Songjun Tang
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Qi Liu
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Hanxiao Shi
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Wei Sun
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, Guangdong, China
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8
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Mokarram M, Taripanah F, Pham TM. Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:122886-122905. [PMID: 37979107 DOI: 10.1007/s11356-023-30859-0] [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: 05/27/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.
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Affiliation(s)
- Marzieh Mokarram
- Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran
| | - Farideh Taripanah
- Department of Desert Control and Management, University of Kashan, Kashan, Iran
| | - Tam Minh Pham
- Research Group On "Fuzzy Set Theory and Optimal Decision-Making Model in Economics and Management", Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
- VNU School of Interdisciplinary Studies, Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
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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.
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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
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Panneerselvam V, Thiagarajan R. ACBiGRU-DAO: Attention Convolutional Bidirectional Gated Recurrent Unit-based Dynamic Arithmetic Optimization for Air Quality Prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:86804-86820. [PMID: 37410321 DOI: 10.1007/s11356-023-28028-4] [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: 12/06/2022] [Accepted: 05/28/2023] [Indexed: 07/07/2023]
Abstract
Over the past decades, air pollution has turned out to be a major cause of environmental degradation and health effects, particularly in developing countries like India. Various measures are taken by scholars and governments to control or mitigate air pollution. The air quality prediction model triggers an alarm when the quality of air changes to hazardous or when the pollutant concentration surpasses the defined limit. Accurate air quality assessment becomes an indispensable step in many urban and industrial areas to monitor and preserve the quality of air. To accomplish this goal, this paper proposes a novel Attention Convolutional Bidirectional Gated Recurrent Unit based Dynamic Arithmetic Optimization (ACBiGRU-DAO) approach. The Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) model is determined in which the fine-tuning parameters are used to enhance the proposed method by Dynamic Arithmetic Optimization (DAO) algorithm. The air quality data of India was acquired from the Kaggle website. From the dataset, the most-influencing features such as Air Quality Index (AQI), particulate matter namely PM2.5 and PM10, carbon monoxide (CO) concentration, nitrogen dioxide (NO2) concentration, sulfur dioxide (SO2) concentration, and ozone (O3) concentration are taken as input data. Initially, they are preprocessed through two different pipelines namely imputation of missing values and data transformation. Finally, the proposed ACBiGRU-DAO approach predicts air quality and classifies based on their severities into six AQI stages. The efficiency of the proposed ACBiGRU-DAO approach is examined using diverse evaluation indicators namely Accuracy, Maximum Prediction Error (MPE), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The simulation result inherits that the proposed ACBiGRU-DAO approach achieves a greater percentage of accuracy of about 95.34% than other compared methods.
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Affiliation(s)
- Vinoth Panneerselvam
- Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
| | - Revathi Thiagarajan
- Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, India
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11
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Zhang L, Liu J, Feng Y, Wu P, He P. PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27630-w. [PMID: 37213020 DOI: 10.1007/s11356-023-27630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/10/2023] [Indexed: 05/23/2023]
Abstract
As the core of pollution prevention and management, accurate PM2.5 concentration prediction is crucial for human survival. However, due to the nonstationarity and nonlinearity of PM2.5 concentration data, the accurate prediction for PM2.5 concentration remains a challenge. In this study, a PM2.5 concentration prediction method using weighted complementary ensemble empirical mode decomposition with adaptive noise (WCEEMDAN) and improved long and short-term memory (ILSTM) neural network is proposed. Firstly, a novel WCEEMDAN method is proposed to correctly identify the non-stationary and non-linear characteristics and divide the PM2.5 sequences into various layers. Through the correlation analysis with PM2.5 data, these sub-layers are given different weights. Secondly, the adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the main hyperparameters of the long short-term memory network (LSTM) neural network, improving the prediction accuracy of PM2.5 concentration. The optimization convergence speed and accuracy are improved by adjusting the inertia weight and introducing the mutation mechanism to enhance the global optimization ability. Finally, three groups of PM2.5 concentration data are utilized to verify the effectiveness of the proposed model. Compared with other methods, the experimental results demonstrate the superiority of the proposed model. The source code can be downloaded from https://github.com/zhangli190227/WCEENDAM-ILSTM .
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Affiliation(s)
- Li Zhang
- School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, China
| | - Jinlan Liu
- School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, China
| | - Yuhan Feng
- School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, China.
| | - Peng Wu
- School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, China
| | - Pengkun He
- Xinyang Meteorological Bureau, Xinyang, China
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12
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Guo Q, He Z, Wang Z. Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors. TOXICS 2023; 11:210. [PMID: 36976975 PMCID: PMC10056825 DOI: 10.3390/toxics11030210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.
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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
- 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
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Guo Q, He Z, Wang Z. Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:22319-22329. [PMID: 36284044 DOI: 10.1007/s11356-022-23813-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Climate change affects air quality and people's health. Therefore, accurate prediction of future climate change is of great significance for human beings to better adapt and mitigate climate change. Using the projection simulation dataset of the CMIP6 multi-model ensemble, the future climate change in the Sahara region under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) is analyzed. The results show that annual and seasonal average surface air temperature in the Sahara region will continue to rise throughout the twenty-first century relative to the baseline period 1995-2014 if greenhouse gas (GHG) concentrations continue increasing. Under the four SSPs scenarios, the warming in the Sahara region will be more pronounced than in the whole world through the twenty-first century. The annual maximum temperature (TX), the annual minimum temperature (TN), the annual count of days with maximum temperature above 35 °C (TX 35), and the annual count of days with maximum temperature above 40 °C (TX 40) in the Sahara region will continue to increase until the end of the twenty-first century under the four scenarios. The results of climate change prediction can provide scientific reference for climate policy-making.
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Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, 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.
- 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.
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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.
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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
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Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4864920. [DOI: 10.1155/2022/4864920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/15/2022] [Accepted: 10/02/2022] [Indexed: 11/11/2022]
Abstract
COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.
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