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A New Paradigm in Flood Management: Hydrological, Hydraulic, and Deep Learning Methods for Flood Routing
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DOI：10.1007/s11269-026-04865-z.png)
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Abstract
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Flood routing is essential for water resources management and flood mitigation, yet traditional methods often struggle to represent complex hydraulic processes and rapidly changing flow conditions. In this study, hydrological models (Muskingum and SCS), hydraulic methods (Kinematic Wave, Muskingum–Cunge, and Dynamic Wave), and deep learning (DL) algorithms (Autoencoder, Deep Neural Network (DNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN)) were applied to predict flood routing. Model performance was evaluated using several statistical indicators, including RMSE, AIC, MAE, KGE, NSE, R², Pbias, and MBE. The results showed that the Autoencoder model provided the best predictive performance (RMSE: 0.30, MAE: 0.17, NSE: 0.97, R²: 0.99), while the RNN model produced the weakest results. Other DL models, particularly DNN, CNN, and LSTM, also demonstrated strong predictive capability. Among the hydraulic approaches, the Kinematic Wave method yielded the most accurate results, whereas the SCS model showed the best performance among hydrological routing methods. Overall, the findings indicate that DL models generally outperform classical routing approaches in flood prediction, with the Autoencoder architecture emerging as the most effective model under the conditions of the present study.
Keywords:
Deep learning
Machine learning
Hydrological models
Hydraulic models
Flood routing
Flood
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