Explainable Deep Learning-Based Prediction of Radar Wave Propagation Loss in Evaporation Ducts and Exploration of Driving Factors

We develop ExPLNet, an explainable deep learning framework for predicting radar wave propagation loss (PL) in non-uniform evaporation ducts, which integrates a hybrid Long Short-Term Memory (LSTM)-Transformer model with SHapley Additive exPlanations (SHAP) technology. ExPLNet employs a data-driven methodology to generate PL predictions by leveraging the LSTM-Transformer framework to discern PL patterns from multi-source driving factors. Subsequently, the SHAP deep explainer module systematically quantifies feature contributions through coalitional game theory, revealing the relative importance of key influencing factors. This architecture simultaneously achieves accurate PL forecasting and physically explainable factor analysis, advancing radio propagation modeling for intelligent communication systems.