Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation - Huu Duy Nguyen; Dinh Kha Dang; Nhu Y Nguyen; Chien Pham Van; Thi Thao Van Nguyen; Quoc-Huy Nguyen; Xuan Linh Nguyen; Le Tuan Pham; Viet Thanh Pham; Quang-Thanh Bui (Journal of Water and Climate Change, 2024)
Flood prediction is an important task, which helps local decision-makers in taking effective measures to reduce damage to the people and economy. Currently, most studies use machine learning to predict flooding in a given region; however, the extrapolation problem is considered a major challenge when using these techniques and is rarely studied. Therefore, this study will focus on an approach to resolve the extrapolation problem in flood depth prediction by integrating machine learning (XGBoost, Extra-Trees (EXT), CatBoost (CB), and light gradient boost machines (LightGBM)) and hydraulic modeling under MIKE FLOOD. The results show that the hydraulic model worked well in providing the flood depth data needed to build the machine learning model. Among the four proposed machine learning models, XGBoost was found to be the best at solving the extrapolation problem in the estimation of flood depth, followed by EXT, CB, and LightGBM. Quang Binh province was hit by floods with depths ranging from 0 to 3.2 m. Areas with high flood depths are concentrated along and downstream of the two major rivers (Gianh and Nhat Le – Kien Giang). HIGHLIGHTS Machine learning and hydrodynamic modeling are used to solve the extrapolation problem.; Model performance was evaluated using root-mean-square error, coefficient of determination (R2), and mean absolute error.; Developed models achieved a high accuracy to solve the extrapolation problem.;