A Review of Flood Hazard Assessment and Risk Zone Identification Using Conventional Physical Models and Machine Learning Algorithms: A Study of the Kalu River Basin

Abstract

The Kalu River Basin in southwestern Sri Lanka has experienced significant flooding, primarily from monsoonal and cyclonic rainfall. Steep topography, rapid land-use changes, and climate change exacerbate flood risk. Notable flood events in this region occurred in 2003, 2008, 2012, 2014, 2017, 2018, and 2021, causing substantial economic losses and social impacts. Traditional forecasting methods have relied on physically based hydrological and hydrodynamic models to simulate rainfall–runoff relationships, particularly in well-instrumented catchments. However, these conventional models often require extensive data, presenting challenges in data-limited environments such as Sri Lanka. Machine learning (ML) models offer an alternative by learning flood-generating patterns directly from hydrological and environmental data, achieving high forecasting accuracy with limited information. In contrast, process-based models encounter challenges such as inflexibility, nonlinear dynamics, and residual biases. This paper reviews global and regional research focusing on hybrid approaches that integrate physical models with ML techniques and assesses their potential application in the Kalu River Basin. The review emphasizes the significance of combining physical and ML models, demonstrating that hybrid methods generally outperform standalone models in estimating peak flows and predicting extreme events, even under data-limited conditions. In summary, this review advocates adopting integrated modeling systems as robust and adaptable solutions for flood forecasting.