Ensemble Machine Learning Approach For Urban Flood Hazard Assessment And Risk Mapping

Flooding is one of the most destructive natural hazards, particularly in urban environments where population density and infrastructure development increase vulnerability to extreme weather events. Accurate identification of flood-prone areas is essential for effective disaster management and urban planning. This study presents an ensemble machine learning framework for urban flood hazard assessment by integrating multiple predictive models. The proposed approach combines the strengths of individual machine learning algorithms such as Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT) to generate a more reliable flood susceptibility map. Several environmental and geographical factors, including slope, elevation, rainfall, land use, and distance to rivers, are analysed to evaluate their influence on flood occurrence. The ensemble model aggregates the predictions of individual models using weighted averaging techniques to improve prediction accuracy and reduce model bias. Experimental results demonstrate that the ensemble approach outperforms individual models in terms of predictive performance and reliability. The generated flood hazard maps provide valuable insights for identifying high-risk zones and supporting decision-makers in developing effective flood mitigation strategies.