文献类型：期刊文献

英文题名：A Haze Prediction Model in Chengdu Based on LSTM

作者：Wu, Xinyi[1];Liu, Zhixin[1];Yin, Lirong[2];Zheng, Wenfeng[3];Song, Lihong[3];Tian, Jiawei[3];Yang, Bo[3];Liu, Shan[3]

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

年份：2021

卷号：12

期号：11

外文期刊名：ATMOSPHERE

收录：SCI-EXPANDED(收录号：WOS:000760016600001)、、EI(收录号：20214611158176)、Scopus(收录号：2-s2.0-85118953441)、WOS

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

语种：英文

外文关键词：haze prediction; multilayer long short-term memory; PM2.5; PM10

外文摘要：Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases' distributions and wind power or temperature are related to PM2.5/10's concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O-3, CO, NO2, SO2, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.