文献类型：期刊文献

英文题名：Spatial and Temporal Prediction of Ozone Concentration in the Pearl River Delta Region Based on a Dynamic Graph Convolutional Network

作者：Yang, Tongshu[1]; Li, Sheng[1]; Chen, Baoqin[1]

机构：[1] Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China

年份：2024

外文期刊名：SSRN

收录：EI(收录号：20240272975)

语种：英文

外文关键词：Anomaly detection - Convolution - Forecasting - Forestry - Ozone - River pollution - Wind speed

外文摘要：The variation of ozone (O3) concentration is closely related to other meteorological factors such as temperature and wind speed, and there is significant dynamic uncertainty, making related research very complex and difficult. This paper will establish a time-space ozone prediction model based on dynamic graph convolution network to study the O3 pollution in the Pearl River Delta (PRD) region of China. Firstly, use an isolated forest (iForest) for anomaly detection in data preprocessing. Secondly, based on data such as wind direction, wind speed, and station geographic location, establish the diffusion distance of the wind field and construct a dynamic graph sequence accordingly. Finally, a spatio-temporal dynamic graph convolutional network (STD-GCN) based on dynamic graph sequences was established for predicting O3 concentration. The experimental results showed that STD-GCN outperformed long short-term memory (LSTM) and graph convolutional embedded LSTM (GC-LSTM). Specifically, by integrating wind field factors, STD-GCN exhibits better spatial interpretability. ? 2024, The Authors. All rights reserved.