Developing an explainable deep learning module based on the LSTM framework for flood prediction - Zhi Zhang; Dagang Wang; Yiwen Mei; Jinxin Zhu; Xusha Xiao (Frontiers in Water)
Long short-term memory (LSTM) networks have become indispensable tools in hydrological modeling due to their ability to capture long-term dependencies, handle non-linear relationships, and integrate multiple data sources but suffer from limited interpretability due to their black box nature. To address this limitation, we propose an explainable module within the LSTM framework, specifically designed for flood prediction across 531 catchments in the contiguous United States. Our approach incorporates a simplified gated module, which is interposed between the input data and the LSTM network, providing a transparent view of the module’s pattern recognition process. This gated module allows for easy identification of key variables and optimal lookback windows, and clusters the gated information into four categories: short-term and long-term impacts of precipitation and temperature. This categorization enhances our understanding of how the module utilizes input data and reveals underlying mechanisms in flood prediction. The modular design of our approach demonstrates high correlation with Saliency method, validating the credibility of its explanatory mechanisms, providing comparable interpretability features to LSTMs while illuminating key variables and optimal lookback windows considered most informative by hydrological models, and opening up new avenues for AI-assisted scientific discovery in the field.