Advancing River Water Level Prediction Using Hybrid and Ensemble Machine Learning Models

Accurately predicting river water levels is essential for effective water resource management and reducing flood risks. Traditional hydrological models often struggle to capture the complex, nonlinear dynamics of river systems. In this study, we explore machine learning techniques to enhance water level predictions. Specifically, we focus on hybrid and ensemble models that combine the strengths of various algorithms to improve both accuracy and reliability. Our approach integrates methods such as Sequential Minimal Optimization for Regression (SMOreg), Rep-Tree, and Decision Table (DT) to predict water levels in the Rhine River. By leveraging hybrid models, we aim to uncover patterns in hydrological data that traditional methods may miss, leading to more precise predictions. The models were trained and validated using 10 years of historical data from the Worms station, incorporating meteorological and hydrological variables as inputs. This study demonstrates that hybrid and ensemble machine learning models offer a robust and reliable solution for predicting river water levels. It underscores the potential of advanced data-driven approaches to support sustainable water resource management and mitigate the impacts of flooding.