Enhancing daily peak water level forecasts in tidal-dominated regions using machine learning and future tide integration

Tidal-dominated coastal urban areas, such as the Dong Nai-Sai Gon River Basin, face escalating flood risks from heavy rainfall, reservoir releases, and tides. Accurate daily peak water level (Zmax) forecasting is vital for flood and traffic management. Conventional harmonic analysis and standard machine learning (ML) methods often overlook future tidal data, limiting predictive accuracy. This study proposes a hybrid framework (M3) integrating 24-hour forecasted tides, derived via Fourier analysis and a multilayer perceptron, with hydrometeorological predictors using ensemble ML. Using data from four stations between 2019 and 2022, the optimal M3-cbr model achieved strong performance, yielding RMSE values from 0.0581 to 0.1103 m, KGE from 0.8499 to 0.9414, WI from 0.9509 to 0.9719, and |PBIAS| < 2.4325%. It improved accuracy by 9% to 43% over M2 and 45% to 86% over M1 during flood seasons. This cost-effective framework supports operational early warning and sustainable flood management.