Predicting the flood peak arrival time via a comprehensive machine-learning framework: case studies in Changhua and Tunxi basins, China

Abstract

Floods are becoming increasingly frequent and severe due to climate change and urbanization, thereby increasing risks to lives, property, and the environment. This necessitates the development of precise flood forecasting systems. This study addresses the critical task of predicting flood peak arrival times, which is essential for timely warnings and preparations, by introducing a comprehensive machine-learning frame-work. Our approach integrates interpretable feature engineering, individual model design, and novel model ensembles to enhance prediction accuracy. We extract informative features from historical flood flow and rainfall data, design a suite of machine-learning models, and develop a novel ensemble technique to combine model predictions. We conducted case studies on the Tunxi and Changhua basins in China. Numerical experiments reveal that our method significantly benefits from feature engineering and model ensembles, achieving mean absolute error (MAE) prediction errors of 1.524 h for Tunxi and 2.192 h for Changhua. These results notably outperform the best baseline method, which achieves MAE errors of 1.727 h for Tunxi and 2.737 h for Changhua.