Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions
Abstract Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear ass...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2020-05-01
|
| Series: | Aerosol and Air Quality Research |
| Subjects: | |
| Online Access: | https://doi.org/10.4209/aaqr.2020.03.0097 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862895245524992 |
|---|---|
| author | Qingchun Guo Zhenfang He Shanshan Li Xinzhou Li Jingjing Meng Zhanfang Hou Jiazhen Liu Yongjin Chen |
| author_facet | Qingchun Guo Zhenfang He Shanshan Li Xinzhou Li Jingjing Meng Zhanfang Hou Jiazhen Liu Yongjin Chen |
| author_sort | Qingchun Guo |
| collection | DOAJ |
| description | Abstract Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi’an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies. |
| format | Article |
| id | doaj-art-6f11249390314b9f8240c5c7b53b583b |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-6f11249390314b9f8240c5c7b53b583b2025-02-09T12:18:55ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-05-012061429143910.4209/aaqr.2020.03.0097Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological ConditionsQingchun Guo0Zhenfang He1Shanshan Li2Xinzhou Li3Jingjing Meng4Zhanfang Hou5Jiazhen Liu6Yongjin Chen7School of Environment and Planning, Liaocheng UniversitySchool of Environment and Planning, Liaocheng UniversitySchool of Environment and Planning, Liaocheng UniversityState Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of SciencesSchool of Environment and Planning, Liaocheng UniversitySchool of Environment and Planning, Liaocheng UniversitySchool of Environment and Planning, Liaocheng UniversitySchool of Environment and Planning, Liaocheng UniversityAbstract Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi’an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies.https://doi.org/10.4209/aaqr.2020.03.0097Air pollutionWavelet artificial neural networkMeteorological factorForecast |
| spellingShingle | Qingchun Guo Zhenfang He Shanshan Li Xinzhou Li Jingjing Meng Zhanfang Hou Jiazhen Liu Yongjin Chen Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions Aerosol and Air Quality Research Air pollution Wavelet artificial neural network Meteorological factor Forecast |
| title | Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions |
| title_full | Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions |
| title_fullStr | Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions |
| title_full_unstemmed | Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions |
| title_short | Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions |
| title_sort | air pollution forecasting using artificial and wavelet neural networks with meteorological conditions |
| topic | Air pollution Wavelet artificial neural network Meteorological factor Forecast |
| url | https://doi.org/10.4209/aaqr.2020.03.0097 |
| work_keys_str_mv | AT qingchunguo airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT zhenfanghe airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT shanshanli airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT xinzhouli airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT jingjingmeng airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT zhanfanghou airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT jiazhenliu airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions AT yongjinchen airpollutionforecastingusingartificialandwaveletneuralnetworkswithmeteorologicalconditions |