A New Paradigm in Flood Management: Hydrological, Hydraulic, and Deep Learning Methods for Flood Routing
Water Resources Management, cilt.40, sa.10, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 40 Sayı: 10
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11269-026-04865-z
- Dergi Adı: Water Resources Management
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Deep learning, Flood, Flood routing, Hydraulic models, Hydrological models, Machine learning
- Erzincan Binali Yıldırım Üniversitesi Adresli: Evet
Özet
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.