AI-Powered Urban Resilience a Dynamic System for Flood Prediction and Active Management

Abstract

As climate change, rapid urbanization, and inadequate drainage infrastructure continue to increase the frequency and severity of urban flooding, intelligent and reliable flood prediction systems have become essential for minimizing disaster impacts and improving urban resilience. This study presents a novel Proposed Hybrid LSTM–XGBoost Flood Prediction Framework for precise and prompt flood risk assessment in disaster management. Multiple sources of data for flood prediction, such as rainfall, river water level, weather, historical flood, and satellite data, are gathered. The data collected are then preprocessed with Min-Max Normalization to enhance data quality, remove scale differences, and make environmental variables consistent. Next, Principal Component Analysis (PCA) is used to select the most salient features and to remove the redundancy to lower the data dimensionality. The optimized feature set is classified using a Hybrid LSTM–XGBoost model that captures temporal dependencies and nonlinear relationships for accurate flood risk prediction. Finally, the proposed framework classifies flood risks as Low, Medium, or High, enabling early warning generation, efficient disaster preparedness, and emergency response. The experimental results show that the proposed framework yields superior prediction accuracy, feature reduction, and offers a powerful and scalable solution for intelligent flood prediction and urban resilience.