A large-scale riverbank erosion risk assessment model integrating multi-source data and explainable artificial intelligence (XAI)

The impact of riverbank erosion poses serious threat to the environment, socio-economics and human safety. Due to the extremely complex mechanisms of erosion, assessing the risk of riverbank erosion is challenging. To address this, we propose an interpretable intelligent model framework to accurately assess large-scale riverbank erosion risk. Firstly, we constructed a multi-source dataset that encompasses 29 riverbank erosion influencing factors. Subsequently, by employing an adaptive feature weighting method, a comprehensive water level factor was synthesized, unifying data dimensions. The Relief algorithm was used to identify influential features for riverbank erosion, and an adaptive feature weighting SMOTE (AFW-SMOTE) algorithm was developed to balance the riverbank erosion dataset. Additionally, an ELM and BiGRU autoencoder was designed to effectively capture and learn key information from static and dynamic features. Finally, the outputs of the two autoencoders were integrated using the XGBoost algorithm to produce riverbank erosion risk assessment results, and the risks were visualized. This model not only performs excellently across multiple evaluation metrics but also significantly surpasses 22 other machine learning models. By integrating the Shapley value method, it enhances the model's interpretability. This provides policymakers and relevant environmental management agencies with a powerful tool to scientifically assess and manage the risk of riverbank erosion.