Edge-Integrated IoT and Computer Vision Framework for Real-Time Urban Flood Monitoring and Prediction

Urban flash floods pose a critical threat to rapidly growing cities in India, where unplanned development, climate variability, and inadequate drainage amplify risks. Guwahati, in Northeast India, experiences recurrent inundation during monsoons, disrupting livelihoods and damaging infrastructure. This study presents an integrated IoT and AI-enabled framework for urban flood monitoring and prediction. A LoRa-based IoT sensor network was deployed to capture localized hydrological and meteorological parameters, overcoming the limitations of coarse weather APIs. Rainfall forecasting was implemented at the edge layer using Random Forest, XGBoost, CatBoost, and K-Nearest Neighbors, fused through a fuzzy logic model that achieved 92.4% accuracy, surpassing individual classifiers. In parallel, a computer vision pipeline detected drainage blockages from geotagged user images, with EfficientNetB0-U-Net achieving ~91% accuracy, outperforming ResNet50, InceptionV3, and MobileNetV2. By combining rainfall prediction, IoT sensing, and blockage detection, the proposed framework delivers a holistic, low-cost, and scalable early warning system, marking a novel contribution toward resilient urban flood management in resource-constrained settings.