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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

文献类型：期刊论文

作者Guo, Qingehun3,4; He, Zhenfang2,4; Li, Shanshan4; Li, Xinzhou1,3; Meng, Jingjing4; Hou, Zhanfang4; Liu, Jiazhen4; Chen, Yongjin4
刊名AEROSOL AND AIR QUALITY RESEARCH
出版日期2020-06-01
卷号20期号:6页码:1429-1439
关键词Air pollution Wavelet artificial neural network Meteorological factor Forecast
ISSN号1680-8584
DOI10.4209/aaqr.2020.03.0097
通讯作者Guo, Qingehun(guoqingchun@lcu.edu.cn) ; He, Zhenfang(hezhenfang@lcu.edu.cn)
英文摘要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 Xian 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 Xian and Lanzhou, although the WANN model (R = 0.8846 for Xian and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xian 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.
WOS关键词MACHINE LEARNING-METHOD ; PM2.5 ; MODEL ; PREDICTION ; POINT
资助项目National Natural Science Foundation of China[41572150] ; National Natural Science Foundation of China[41472162] ; National Natural Science Foundation of China[41702373] ; Shandong Social Sciences Planning Research Program[18CKPJ34] ; Shandong Province Higher Educational Humanities and Social Science Program[J18RA196] ; State Key Laboratory of Loess and Quaternary Geology Foundation[SKLLQG1907]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000537943300022
出版者TAIWAN ASSOC AEROSOL RES-TAAR
资助机构National Natural Science Foundation of China ; Shandong Social Sciences Planning Research Program ; Shandong Province Higher Educational Humanities and Social Science Program ; State Key Laboratory of Loess and Quaternary Geology Foundation
源URL[http://ir.ieecas.cn/handle/361006/14853]  
专题地球环境研究所_古环境研究室
通讯作者Guo, Qingehun; He, Zhenfang
作者单位1.CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
3.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
4.Liaocheng Univ, Sch Environm & Planning, Liaocheng 252000, Shandong, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Qingehun,He, Zhenfang,Li, Shanshan,et al. Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions[J]. AEROSOL AND AIR QUALITY RESEARCH,2020,20(6):1429-1439.
APA Guo, Qingehun.,He, Zhenfang.,Li, Shanshan.,Li, Xinzhou.,Meng, Jingjing.,...&Chen, Yongjin.(2020).Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions.AEROSOL AND AIR QUALITY RESEARCH,20(6),1429-1439.
MLA Guo, Qingehun,et al."Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions".AEROSOL AND AIR QUALITY RESEARCH 20.6(2020):1429-1439.

入库方式： OAI收割

来源：地球环境研究所

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