Multi-site Ozone Mass Concentration Prediction Model Based on MSGSFE-TAGRU
ZHEN Diandian
TANG Chaoli
ZHU Zhenye
Abstract：To address the limitations in spatiotemporal feature modeling and the insufficient capture of temporal dependencies in ozone mass concentration prediction,a multi-scale graph spatial feature extraction-temporal attention enhanced gated recurrent unit(MSGSFE-TAGRU)prediction model was proposed.The model consisted of two main modules:MSGSFE and TAGRU modules.In the MSGSFE module,multi-scale graph structures were constructed by integrating graph attention network(GAT)and graph convolutional network(GCN)to effectively extract both local and global spatial features.In the TAGRU module,a temporal attention mechanism was introduced to dynamically focus on key historical time steps,which enhanced the capability of the gated recurrent unit(GRU)in modeling for temporal dependencies.The results demonstrated that,in short-term prediction,the MSGSFE-TAGRU model reduced root mean square error and mean absolute error by 5.22%and 5.76%,respectively,compared to the best-performing graph neural network-GRU(GNN-GRU)model.In medium-and long-term prediction,the proposed model continued to exhibit superior accuracy and stability,validating its effectiveness in spatiotemporal feature modeling and generalization.This study provided a novel methodological framework for high-precision prediction of ozone mass concentration and air quality management.
Keywords：ozone mass concentration predictionmulti-scale graph convolutiondynamic temporal attention mechanismspatiotemporal prediction modelBeijing City
Publication Date：2025-09-20
Online Publishing Date：2025-09-24(First online date of this platform, not the publication date of the document)
Pages：6( 425-430 )