Sustainable Early Warning System for Urban Flood Risk Assessment using Hybrid CNN-LSTM and Remote Sensing Data

Abstract

Urban flooding is a major issue to sustainable urban development and disaster risk mitigation. A sustainable AI-based early warning system is suggested to improve the predictive effectiveness and reactiveness and is based on a hybrid deep learning architecture to provide real-time flood hazard estimates. It combines Long Short-Term Memory (LSTM) networks to perform temporal analysis of rainfall data and Convolutional Neural Networks (CNN) to extract spatial features of images in small patches of remote sensing. The hybrid model is effective in describing the multi-modal characteristics of the flood-related data, and the classification performance is improved. Synthetic rainfall sequences and satellite-like images have been evaluated experimentally with an overall accuracy of 94, an F1-score of 0.94 and area under the ROC curve (AUC) of 0.97. Such findings suggest that the generalization and reliability of flood versus non-flood situations are strong. The solution is appropriately designed to be implemented in real time on edge or cloud computing platforms to implement scalable, intelligent and interpretable flood monitoring systems. This framework provides a practical base of future sustainable and proactive urban flood risk management.