urban flood risk prediction with machine learning models

Urban flooding poses a significant threat to cities worldwide, causing extensive damage to infrastructure and endangering human lives. Predicting flood risk in urban areas is crucial for effective disaster management and mitigation strategies. In this paper, we propose a novel approach to urban flood risk prediction using machine learning models. We collected and preprocessed a comprehensive dataset comprising various environmental, hydrological, and socio-economic factors. Our study utilized a range of machine learning models, including decision trees, random forests, support vector machines, and neural networks, to predict flood risk in urban areas. Through rigorous model training and evaluation, we assessed the performance of these models using metrics such as accuracy, precision, recall, and AUC-ROC. Our results demonstrate the effectiveness of machine learning models in accurately predicting flood risk in urban settings, outperforming baseline models and existing approaches. However, we acknowledge certain limitations, such as data quality and feature selection, which warrant further investigation. This research contributes to the growing body of knowledge in urban flood risk prediction and highlights the potential of machine learning models in enhancing flood preparedness and response strategies. Future research should focus on refining the models and incorporating real-time data for more accurate and timely predictions.