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

CORC > 生态环境研究中心 > 中国科学院生态环境研究中心 > 城市与区域生态国家重点实验室

	Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions
	Guo, Qingehun; He, Zhenfang; Li, Shanshan; Li, Xinzhou; Meng, Jingjing; Hou, Zhanfang; Liu, Jiazhen; Chen, Yongjin
刊名	AEROSOL AND AIR QUALITY RESEARCH
	2020-06
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
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.
内容类型	期刊论文
源URL	[http://ir.rcees.ac.cn/handle/311016/44203]
专题	生态环境研究中心_城市与区域生态国家重点实验室
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.