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Vachon J, Kerckhoffs J, Buteau S, Smargiassi A. Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review. ENVIRONMENTAL RESEARCH 2024:119751. [PMID: 39117059 DOI: 10.1016/j.envres.2024.119751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 07/18/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND & OBJECTIVE The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO2), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions. METHODS Web of Science and Scopus were searched up to June 13, 2024. All records were screened by two independent reviewers. Differences in the coefficient of determination (R2) and Root Mean Square Error (RMSE) between best statistical and machine learning methods were compared across categories of methodological elements. RESULTS A total of 38 studies with 46 model comparisons (30 for NO2, 8 for UFPs and 8 for BC) were included. Linear non-regularized methods and Random Forest were most frequently used. Machine learning outperformed statistical models in 34 comparisons. Mean differences (95% confidence intervals) in R2 and RMSE between best machine learning and statistical models were 0.12 (0.08, 0.17) and 20% (11%, 29%) respectively. Tree-based methods performed best in 12 of 17 multi-model comparisons. Nonlinear or regularization regression methods were used in only 12 comparisons and provided similar performance to machine learning methods. CONCLUSION This systematic review suggests that machine learning methods, especially tree-based methods, may be superior to linear non-regularized methods for predicting ambient concentrations of NO2, UFPs and BC. Additional comparison studies using nonlinear, regularized and a wider array of machine learning methods are needed to confirm their relative performance. Future air pollution studies would also benefit from more explicit and standardized reporting of methodologies and results.
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Affiliation(s)
- Julien Vachon
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Stéphane Buteau
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Audrey Smargiassi
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
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Pazhanivel DB, Velu AN, Palaniappan BS. Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway. SENSORS (BASEL, SWITZERLAND) 2024; 24:5069. [PMID: 39124116 PMCID: PMC11315033 DOI: 10.3390/s24155069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.
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Affiliation(s)
| | - Anantha Narayanan Velu
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India; (D.B.P.); (B.S.P.)
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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.
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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.
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Li R, Zhao J, Feng K, Tian Y. Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170777. [PMID: 38331278 DOI: 10.1016/j.scitotenv.2024.170777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.
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Affiliation(s)
- Rumei Li
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Jinghao Zhao
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Kun Feng
- Shanxi Low-carbon Environmental Protection Industry Group Co., Ltd., Taiyuan 030012, China; Shanxi Ecological Environment Monitoring Center, Taiyuan 030027, China
| | - Yajun Tian
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China.
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Kovtun V, Grochla K, Kharchenko V, Haq MA, Semenov A. Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic. Sci Rep 2023; 13:22810. [PMID: 38129492 PMCID: PMC10739954 DOI: 10.1038/s41598-023-49007-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.
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Affiliation(s)
- Viacheslav Kovtun
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland.
| | - Krzysztof Grochla
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
| | | | - Mohd Anul Haq
- College of Computer and Information Sciences, Majmaah University, Al Majma'ah, Saudi Arabia
| | - Andriy Semenov
- Vinnytsia National Technical University, Vinnytsia, Ukraine
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Wang H, Zhang L, Wu R, Cen Y. Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach. ENVIRONMENTAL RESEARCH 2023; 239:117286. [PMID: 37797668 DOI: 10.1016/j.envres.2023.117286] [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: 07/17/2023] [Revised: 09/29/2023] [Accepted: 09/30/2023] [Indexed: 10/07/2023]
Abstract
In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors. To address this issue, we introduce an innovative deep-learning-based multi-graph model using Beijing as the study case. This model consists of two key modules: firstly, the 'Meteorological Factor Spatio-Temporal Feature Extraction Module'. This module deeply integrates spatio-temporal features of hourly meteorological data by employing Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) for spatial and temporal encoding respectively. Subsequently, through an attention mechanism, it retrieves a feature tensor associated with air pollutants. Secondly, these features are amalgamated with PM2.5 concentration values, allowing the 'PM2.5 Concentration Prediction Module' to predict with enhanced accuracy the joint influence across multiple monitoring sites. Our model exhibits significant advantages over traditional methods in processing the joint impact of multiple sites and their associated meteorological factors. By providing new perspectives and tools for the in-depth understanding of urban air pollutant distribution and optimization of air quality management, this model propels us towards a more comprehensive approach in tackling air pollution issues.
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Affiliation(s)
- Hongqing Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Lifu Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
| | - Rong Wu
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Yi Cen
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, 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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Chang-Silva R, Tariq S, Loy-Benitez J, Yoo C. Smart solutions for urban health risk assessment: A PM 2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network. CHEMOSPHERE 2023:139071. [PMID: 37271471 DOI: 10.1016/j.chemosphere.2023.139071] [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: 04/28/2023] [Accepted: 05/28/2023] [Indexed: 06/06/2023]
Abstract
Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM2.5 monitoring network and simultaneously forecasts PM2.5 concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels. First, this study uses a graph convolutional layer to incorporate the spatial relationship of the neighboring stations at their current state with real-time measurements. Then, it is coupled to a deep learning temporal model to form the long- and short-term time-series graph convolutional network (LSTGraphNet) model, anticipating high pollutant concentration events. This work tested the proposed model with a case study of an existing ambient air quality monitoring network in South Korea. LSTGraphNet model showed prediction performances of PM2.5 at multiple monitoring stations with a mean absolute error (MAE) of 1.82 μg/m3, 4.46 μg/m3, and 4.87 μg/m3 for forecasting horizons of one, three, and 6 h ahead, respectively. Compared to conventional sequential models, this architecture was superior among the state-of-the-art baselines, where the MAE decreased to 41%, respectively. The results of the study showed that the proposed architecture was superior to conventional sequential models and could be used as a tool for decision-making in smart cities by revealing hotspots of higher and lower PM2.5 concentrations in the long term.
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Affiliation(s)
- Roberto Chang-Silva
- Integrated Engineering, Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea
| | - Shahzeb Tariq
- Integrated Engineering, Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea
| | - Jorge Loy-Benitez
- Integrated Engineering, Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea; Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - ChangKyoo Yoo
- Integrated Engineering, Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea.
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Shen Y, Ma X, Sun Y, Du S. Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor. PLoS One 2023; 18:e0286325. [PMID: 37228158 DOI: 10.1371/journal.pone.0286325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/15/2023] [Indexed: 05/27/2023] Open
Abstract
Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.
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Affiliation(s)
- Yueqian Shen
- Department of Finance and Asset Management, China University of Geosciences, Wuhan, China
| | - Xiaoxia Ma
- Department of Finance and Asset Management, China University of Geosciences, Wuhan, China
| | - Yajing Sun
- Audit Department, China University of Geosciences, Wuhan, China
| | - Sheng Du
- School of Automation, China University of Geosciences, Wuhan, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
- Shenzhen ET Technology Co., Ltd, Shenzhen, China
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Rakholia R, Le Q, Quoc Ho B, Vu K, Simon Carbajo R. Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam. ENVIRONMENT INTERNATIONAL 2023; 173:107848. [PMID: 36842381 DOI: 10.1016/j.envint.2023.107848] [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: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to surpass the WHO standard, which has become a very serious problem affecting human health and the ecosystem. Various machine learning algorithms have recently been widely used in air quality forecasting studies to predict possible impacts. Training and constructing several machine learning models for different air pollutants, such as NO2, SO2, O3, and CO forecasts, is a time-consuming process that necessitates additional effort for deployment, maintenance, and monitoring. In this paper, an effort has been made to develop a multi-step multi-output multivariate model (a global model) for air quality forecasting, taking into account various parameters such as meteorological conditions, air quality data from urban traffic, residential, and industrial areas, urban space information, and time component for the prediction of NO2, SO2, O3, CO hourly (1 h to 24 h) concentrations. The global forecasting model can anticipate multiple air pollutant concentrations concurrently, based on past concentrations of covariate characteristics. The datasets on air pollution time series were gathered from six HealthyAir air quality monitoring sites in HCMC between February 2021 and August 2022. Darksky weather provided the hourly concentrations of meteorological conditions for the same period. This is the first model built using real-time air quality data for NO2, SO2, CO, and O3 forecasting in HCM city. To assess the effectiveness of the proposed model, it was evaluated using real data from HealthyAir stations and quantified using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation indices. The results show that the global air quality forecasting model beats earlier models built for air quality forecasting of each specific pollutant in HCMC.
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Affiliation(s)
- Rajnish Rakholia
- Ireland's National Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
| | - Quan Le
- Ireland's National Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland
| | - Bang Quoc Ho
- Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam; Department of Science and Technology, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Khue Vu
- Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam
| | - Ricardo Simon Carbajo
- Ireland's National Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland
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Xiao L, Antczak P, Rüegg J, Behrendt L. Zeitotox: Toxicology and the Rhythms of Life. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11100-11102. [PMID: 35895873 PMCID: PMC9387090 DOI: 10.1021/acs.est.2c02961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Linhong Xiao
- Science
for Life Laboratory, Department of Organismal Biology, Uppsala University, Norbyv. 18A, 75236 Uppsala, Sweden
| | - Philipp Antczak
- Center
for Molecular Medicine Cologne, Lab. of Computational Biology of Ageing, University of Cologne, Robert-Koch-Str. 21, 50931 Cologne, Germany
| | - Joëlle Rüegg
- Science
for Life Laboratory, Department of Organismal Biology, Uppsala University, Norbyv. 18A, 75236 Uppsala, Sweden
| | - Lars Behrendt
- Science
for Life Laboratory, Department of Organismal Biology, Uppsala University, Norbyv. 18A, 75236 Uppsala, Sweden
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Tong Y, Liu J, Yu L, Zhang L, Sun L, Li W, Ning X, Xu J, Qin H, Cai Q. Technology investigation on time series classification and prediction. PeerJ Comput Sci 2022; 8:e982. [PMID: 35634126 PMCID: PMC9138170 DOI: 10.7717/peerj-cs.982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 06/01/2023]
Abstract
Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data. In recent years, many scholars have achieved fruitful results in the study of time series. A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction. Therefore, in this study, we focus on analyzing the technical development routes of time series classification and prediction algorithms. 87 literatures with high relevance and high citation are selected for analysis, aiming to provide a more comprehensive reference base for interested researchers. For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks. For time series prediction, from classical statistical methods, to neural network methods, and then to fuzzy modeling and transfer learning methods, the performance and applications of these different methods are discussed. We hope this article can help aid the understanding of the current development status and discover possible future research directions, such as exploring interpretability of time series analysis and online learning modeling.
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Affiliation(s)
- Yuerong Tong
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Jingyi Liu
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Lina Yu
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Liping Zhang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Linjun Sun
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Weijun Li
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
- Shenzhen DAPU Microelectronics Co., Ltd., Shenzhen, China
| | - Xin Ning
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Jian Xu
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Hong Qin
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Qiang Cai
- National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, China
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13
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Sun B, Bai J, Chu X, Sun S, Li Y, Li H. Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Espinosa F, Bartolomé AB, Hernández PV, Rodriguez-Sanchez MC. Contribution of Singular Spectral Analysis to Forecasting and Anomalies Detection of Indoors Air Quality. SENSORS (BASEL, SWITZERLAND) 2022; 22:3054. [PMID: 35459037 PMCID: PMC9028519 DOI: 10.3390/s22083054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
The high impact of air quality on environmental and human health justifies the increasing research activity regarding its measurement, modelling, forecasting and anomaly detection. Raw data offered by sensors usually makes the mentioned time series disciplines difficult. This is why the application of techniques to improve time series processing is a challenge. In this work, Singular Spectral Analysis (SSA) is applied to air quality analysis from real recorded data as part of the Help Responder research project. Authors evaluate the benefits of working with SSA processed data instead of raw data for modelling and estimation of the resulting time series. However, what is more relevant is the proposal to detect indoor air quality anomalies based on the analysis of the time derivative SSA signal when the time derivative of the noisy original data is useless. A dual methodology, evaluating level and dynamics of the SSA signal variation, contributes to identifying risk situations derived from air quality degradation.
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Affiliation(s)
- Felipe Espinosa
- Electronics Department, University of Alcala, E-28801 Alcalá de Henares, Spain; (F.E.); (A.B.B.)
| | - Ana B. Bartolomé
- Electronics Department, University of Alcala, E-28801 Alcalá de Henares, Spain; (F.E.); (A.B.B.)
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15
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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.
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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
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16
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Xu RQ, Zhou MY, Liao H. PNR: How to optimally combine different link prediction approaches? Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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17
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Wang Z, Chen H, Zhu J, Ding Z. Daily PM2.5 and PM10 forecasting using linear and nonlinear modeling framework based on robust local mean decomposition and moving window ensemble strategy. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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