Coupling a Physically Based Hydrological Model with a Modified Transformer for Long-Sequence Runoff and Peak-Flow Prediction

Yicheng Gu, Bing Yan (), Siru Wang, Zhao Cai and Hongwei Liu
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Yicheng Gu: Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Bing Yan: Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Siru Wang: Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Zhao Cai: Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Hongwei Liu: Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China

Sustainability, 2025, vol. 17, issue 19, 1-26

Abstract: Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions.

Keywords: transformer; extreme flood events; hydrological model; hybrid model; data-driven model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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