Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture
Yuchen Wang,
Zhengshan Luo,
Yulei Kong
et al.
Abstract:This study addressed the limitations of traditional methods in predicting air pollution dispersion, which include restrictions in handling spatiotemporal dynamics, unbalanced feature importance, and data scarcity. To overcome these challenges, this research introduces a novel deep learning-based model, SAResNet-TCN, which integrates the strengths of a Residual Neural Network (ResNet) and a Temporal Convolutional Network (TCN). This fusion is designed to effectively capture the spatiotemporal characteristics an… Show more
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