中国矿业大学主页平台系统俞和胜--中文主页-- Multi-step forecast of PM2. 5 and PM10 concentrations using convolutional neural network integrated with spatial

论文成果

Multi-step forecast of PM2. 5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning

发布时间：2023-01-30点击次数：

影响因子：13.352
DOI码：10.1016/j.envint.2022.107691
发表刊物：Environment International
关键字：PM concentration forecasting；Deep learning；Spatial-temporal attention；Residual learning；Convolutional neural network
摘要：Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.
论文类型：期刊论文
论文编号：107691
卷号：171
是否译文：否
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0160412022006183