文献类型：期刊文献

英文题名：Haze Prediction Model Using Deep Recurrent Neural Network

作者：Shang, Kailin[1];Chen, Ziyi[1];Liu, Zhixin[1];Song, Lihong[2];Zheng, Wenfeng[2];Yang, Bo[2];Liu, Shan[2];Yin, Lirong[3]

机构：[1]Shaoxing Univ, Sch Life Sci, Shaoxing 312000, Peoples R China;[2]Univ Elect Sci & Technol China, Sch Automat, Chengdu 610054, Peoples R China;[3]Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA

年份：2021

卷号：12

期号：12

外文期刊名：ATMOSPHERE

收录：SCI-EXPANDED(收录号：WOS:000735468900001)、、EI(收录号：20215211401982)、Scopus(收录号：2-s2.0-85121682399)、WOS

基金：FundingThis work was jointly supported by the Sichuan Science and Technology Program (2021YFQ0003).

语种：英文

外文关键词：haze prediction; PM10; deep recurrent neural network

外文摘要：In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.