マルチソースリモートセンシングデータ統合とハイブリッド深層学習フレームワークによる大気質予測

Growing pollution levels and associated detrimental effects on human health have made air quality monitoring and forecasting crucial issues in urban environmental management. This paper introduces a novel approach to air quality prediction by using a hybrid deep learning framework that combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) with multi-source remote sensing data. We introduce a Multi-Modal Attention-based Spatio-Temporal Network (MAST-Net) designed to jointly analyse satellite imagery, meteorological variables and ground observations for predicting concentrations of PM2.5, PM10, NO₂, and O₃. The framework leverages data from Sentinel-5P, MODIS, and Landsat-8 satellites, integrates a dynamic feature selection strategy, and incorporates uncertainty quantification to enhance reliability. When compared to conventional approaches, experimental validation over metropolitan areas demonstrates higher performance with RMSE improvements of 23-31%, reaching correlation coefficients of 0.91-0.94 for various contaminants. With its strong prediction capabilities across a range of geographic and seasonal situations, the suggested architecture has great promise for real-time air quality control systems.