An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants
Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/13/7/1055 |
| _version_ | 1797440946773688320 |
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| author | Ching-Hsue Cheng Ming-Chi Tsai |
| author_facet | Ching-Hsue Cheng Ming-Chi Tsai |
| author_sort | Ching-Hsue Cheng |
| collection | DOAJ |
| description | Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality. |
| first_indexed | 2024-03-09T12:15:47Z |
| format | Article |
| id | doaj.art-5eeaff4172d74008bac3cc9755b3d89d |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T12:15:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-5eeaff4172d74008bac3cc9755b3d89d2023-11-30T22:46:42ZengMDPI AGAtmosphere2073-44332022-07-01137105510.3390/atmos13071055An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air PollutantsChing-Hsue Cheng0Ming-Chi Tsai1Department of Information Management, National Yunlin University of Science and Technology, Douliu 640301, TaiwanDepartment of Business Administration, I-Shou University; Kaohsiung City 840301, TaiwanRapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality.https://www.mdpi.com/2073-4433/13/7/1055air pollutionvariable selectionautoregressive distributed lag modelair quality rulestime-series forecast model |
| spellingShingle | Ching-Hsue Cheng Ming-Chi Tsai An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants Atmosphere air pollution variable selection autoregressive distributed lag model air quality rules time-series forecast model |
| title | An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants |
| title_full | An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants |
| title_fullStr | An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants |
| title_full_unstemmed | An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants |
| title_short | An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants |
| title_sort | intelligent time series model based on hybrid methodology for forecasting concentrations of significant air pollutants |
| topic | air pollution variable selection autoregressive distributed lag model air quality rules time-series forecast model |
| url | https://www.mdpi.com/2073-4433/13/7/1055 |
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