Hybrid AI and Storage Function Model for Accurate Flood Hydrograph Prediction During Typhoon Rainfall Events

Accurate flood hydrograph prediction during typhoon-induced heavy rainfall events is crucial for flood risk management, particularly in critical catchments such as the Shihmen Reservoir watershed in Taiwan. The Shihmen Reservoir plays a pivotal role in flood control, water supply, and hydroelectric power generation, making reliable flow predictions essential for its effective operation during extreme weather events.

This study addresses the challenges of long-duration flood hydrograph prediction by developing a hybrid model that integrates an AI-based Rainfall-Runoff Autoregressive with Exogenous Inputs (RNARX) model and a hydrological storage function model. While the RNARX model effectively estimates flow during active rainfall periods using rainfall as the primary input, its performance diminishes post-rainfall when rainfall values drop to zero, leading to rapid underestimation of flow. In contrast, the storage function model provides reliable flow predictions during the recession phase but tends to overestimate flows during intense rainfall events.

By seamlessly combining these two models and defining conditions for model transitions, the hybrid approach ensures robust performance across the entire flood hydrograph. Applied to the Shihmen Reservoir watershed, the hybrid model demonstrates significant improvements in predicting long-duration flood flows, particularly for high-intensity typhoon rainfall events.

This integrated modeling approach enhances real-time flood forecasting, offering valuable insights for optimizing reservoir operations and mitigating flood risks in the Shihmen Reservoir watershed, a region of critical hydrological and socio-economic importance.

 

Keywords: Hybrid Modeling, Artificial Intelligence (AI),Storage Function Model, Flood Hydrograph Prediction, Flood Risk Management