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Enhancing urban flood hazard assessment: A comparative analysis of frequency ratio and xgboost models for precision risk mapping

El Haou Mohamed, Ourribane Malika, Ismaili Maryem, Krimissa Samira, Namous Mustapha

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 3

265--284

Urban flood hazard prediction should effectively balance accuracy and interpretability. This paper compares the performances of the Frequency Ratio method, a simple statistical technique, and XGBoost, a state-of-the-art machine learning algorithm for flood Hazard mapping in Beni Mellal (Morocco). The dataset was derived from preprocessed and standardized Sentinel-2 and Landsat 8 images, a Digital Elevation Model, and geological and soil maps. A flood inventory map was produced, it was then divided into training and testing subsets in the ratio of 70:30 for model calibration and validation, respectively. The FR method highlights key geographical variables such as slope, proximity to rivers, and vegetation indices to deliver rapid, interpretable flood risk assessments. In contrast, XGBoost captures complex, nonlinear relationships by integrating natural and anthropogenic factors for precise risk mapping. The results indicate that while FR is efficient for preliminary assessments in data-scarce environments, XGBoost significantly outperforms it in accuracy, reliability, and detailed hazard differentiation. XGBoost achieved an area under the curve (AUC) of 90.71% in testing datasets compared to 86.1% for FR. Flood distribution analysis showed that FR identified 21.3% of the study area as low-risk and 11.3% as very high-risk, suitable for broad evaluations. XGBoost, however, mapped 73.0% as very low-risk and 12.0% as very high-risk, making it valuable for resource-efficient interventions. This study highlights the complementary strengths of both approaches and advocates for integrating FR’s rapid insights with XGBoost’s precision. Together, they provide a robust framework for comprehensive flood hazard management in semi-arid regions, balancing strategic planning with localized interventions.