A Review of XAI Methods Applications in Forecasting Runoff and Water Level Hydrological Tasks

Andrei M. Bramm (), Pavel V. Matrenin and Alexandra I. Khalyasmaa
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Andrei M. Bramm: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia
Pavel V. Matrenin: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia
Alexandra I. Khalyasmaa: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia

Mathematics, 2025, vol. 13, issue 17, 1-26

Abstract: Modern artificial intelligence methods are increasingly applied in hydrology, particularly for forecasting water inflow into reservoirs. However, their limited interpretability constrains practical deployment in critical water resource management systems. Explainable AI offers solutions aimed at increasing the transparency of models, which makes the topic relevant in the context of developing sustainable and trusted AI systems in hydrology. Articles published in leading scientific journals in recent years were selected for the review. The selection criteria were the application of XAI methods in hydrological forecasting problems and the presence of a quantitative assessment of interpretability. The main attention is paid to approaches combining LSTM, GRU, CNN, and ensembles with XAI methods such as SHAP, LIME, Grad-CAM, and ICE. The results of the review show that XAI mechanisms increase confidence in AI forecasts, identify important meteorological features, and allow analyzing parameter interactions. However, there is a lack of standardization of interpretation, especially in problems with high-dimensional input data. The review emphasizes the need to develop robust, unified XAI approaches that can be integrated into next-generation hydrological models.

Keywords: XAI; runoff; inflow; streamflow; forecasting; AI forecasting models; SHAP; LIME; Grad-CAM; ICE; attention mechanisms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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