Spatio-temporal feature interpretable model for air quality forecasting

Scientific quantitative description of air quality uncertainty remains a challenging task. The key to accurate and interpretable forecasting of air quality lies in combining spatial and temporal information and feature interpretability. This study proposes a novel spatio-temporal forecasting model based on deep learning with squeeze and excitation attention mechanism and interpretable feature selection strategy. Specifically, the data is collected from Beijing-Tianjin-Hebei region which is highly representative and convenient for accuracy comparison. And interpretable feature selection model effectively combines the muti-stage feature selection and Shapley additive explanation methods to identify individual features that contribute significantly to the forecasting target as base variable. Subsequently, a deterministic forecasting model based on improved deep learning with squeeze and excitation channel attention to adaptively adjust feature weights is constructed to implement air quality forecasting. Finally, feature interpretable model analyzes the changes in different air quality influencing factors as time advances, providing policy guidance for joint prevention and control of regional air pollution. Experimental and discussion results reveals that the proposed model can serve as an effective air pollution management and analysis tool.