文献类型：期刊文献

英文题名：Predict the effect of meteorological factors on haze using BP neural network

作者：Chen, Jie[1];Liu, Zhixin[1];Yin, Zhengtong[2];Liu, Xuan[3];Li, Xiaolu[4];Yin, Lirong[5];Zheng, Wenfeng[6]

机构：[1]Shaoxing Univ, Sch Life & Environm Sci, Shaoxing 312000, Peoples R China;[2]Guizhou Univ, Coll Resource & Environm Engn, Guiyang 550025, Peoples R China;[3]Univ Elect Sci & Technol China, Sch Publ Affairs & Adm, Chengdu 611731, Sichuan, Peoples R China;[4]Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China;[5]Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA;[6]Univ Elect Sci & Technol China, Sch Automat, Chengdu 610054, Peoples R China

年份：2023

卷号：51

外文期刊名：URBAN CLIMATE

收录：SCI-EXPANDED(收录号：WOS:001050954700001)、、Scopus(收录号：2-s2.0-85166200151)、WOS

基金：Funding Supported by Sichuan Science and Technology Program (2023YFSY0026, 2023YFH0004) , Zhejiang Xinmiao Talents Program (2022R432A009, 2023R465008) .

语种：英文

外文关键词：Haze; Data mining; Impact factor; Pearson correlation; BP neural network; PM2; 5

外文摘要：Rapid urbanization and economic growth in China have resulted in severe haze. PM2.5 is a key component of haze. Using machine learning to predict PM2.5 concentrations has become a research hotspot. There are few previous studies on the correlation between PM2.5 and meteorological factors, and the accuracy of other detection results is not high. This study investigates the correlation between six meteorological factors (temperature, dew point, humidity, barometric pressure, wind speed, and visibility) and hourly PM2.5 concentration in Beijing. Pearson correlation analysis was used to analyze and found that meteorological factors had a stable non-linear effect on the change in PM2.5 concentration. Additionally, a BP neural network model was employed to examine the influence of meteorological conditions on hazy weather, considering both univariate and multifactorial aspects. Experimental results reveal a strong correlation between PM2.5 concentration and the four seasons, exhibiting minimal deviation and varying prediction errors throughout the seasons, confirming the seasonal association with haze weather. Consequently, the utilization of the BP neural network model for air quality index prediction is considered feasible. This research serves as a valuable reference for understanding the formation mechanism, implementing control methods, and establishing a haze prediction system to effectively manage urban air quality.