计算机与现代化Issue(1)：17-22,6.DOI:10.3969/j.issn.1006-2475.2026.01.003
大气污染物浓度时空分布预测的深度学习模型
Deep Learning Model for Spatiotemporal Distribution Prediction of Air Pollutant Concentrations
摘要
Abstract
Aiming at the problem that existing models are difficult to capture temporal dependencies and are lack of spatial dis-tributing features in atmospheric pollutant concentration predictions,we propose a multi-scale convolutional long short-term memory(IncConvLSTM)model.This model integrates multi-scale convolution from the Inception model into the ConvLSTM net-work.Using hourly pollutant data from Dongguan(2021-2022)and incorporating grid map generating by using ground,satel-lite,and meteorological data,we predict PM2.5,O3,NO2,SO2,and CO concentrations hourly at a 1 km2 resolution,and com-pared to ConvGRU,ConvLSTM,and CNN-LSTM models.Experimental results show that IncConvLSTM achieves higher R2 val-ues(0.87,0.90,0.85,0.80,0.77)and better prediction accuracy,stability,ability to cope with sudden changes.This model offers a novel robust and effective method for fine-scale spatiotemporal pollution predictions.关键词
大气污染物预测/网格图/深度学习/IncConvLSTM模型/Inception模型/ConvLSTM模型Key words
atmospheric pollutant prediction/grid map/deep learning/IncConvLSTM model/Inception model/ConvLSTM model分类
信息技术与安全科学引用本文复制引用
苏沛,张建坤..大气污染物浓度时空分布预测的深度学习模型[J].计算机与现代化,2026,(1):17-22,6.基金项目
江西省自然科学基金资助项目(20202BAB204035) （20202BAB204035）
江西省核地学数据科学与系统工程技术研究中心开放基金资助项目(JETRCNGDSS202103) （JETRCNGDSS202103）
