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

doi:10.4209/aaqr.2020.03.0097

IEECAS OpenIR > 古环境研究室

	Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions
	Guo, Qingehun 1,2; He, Zhenfang 1,3; Li, Shanshan 1; Li, Xinzhou2,4 ; Meng, Jingjing 1; Hou, Zhanfang 1; Liu, Jiazhen 1; Chen, Yongjin 1
通讯作者	Guo, Qingehun(guoqingchun@lcu.edu.cn) ; He, Zhenfang(hezhenfang@lcu.edu.cn)
	2020-06-01
发表期刊	AEROSOL AND AIR QUALITY RESEARCH
ISSN	1680-8584
卷号	20 期号:6 页码:1429-1439
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.
关键词	Air pollution Wavelet artificial neural network Meteorological factor Forecast
DOI	10.4209/aaqr.2020.03.0097
关键词[WOS]	MACHINE LEARNING-METHOD ; PM2.5 ; MODEL ; PREDICTION ; POINT
收录类别	SCI ; SCI
语种	英语
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
项目资助者	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
WOS类目	Environmental Sciences
WOS记录号	WOS:000537943300022
出版者	TAIWAN ASSOC AEROSOL RES-TAAR
引用统计	被引频次：37[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ieecas.cn/handle/361006/14854
专题	古环境研究室
通讯作者	Guo, Qingehun; He, Zhenfang
作者单位	1.Liaocheng Univ, Sch Environm & Planning, Liaocheng 252000, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China 3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China 4.CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, 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.