AI-Powered Early Warning Systems for Urban Flood Management: Integrating Climate Models and Real-Time Sensor Data

Abstract

Flooding within the urban areas is one of the greatest risks to human life, infrastructure, and economic stability particularly in cities that are rapidly populated with ineffective drainage systems and high exposure to extreme weather patterns. Conventional flood management systems, which frequently are dependent on a fixed system of hydrological models and slow reporting, cannot be able to predicate or act fast enough when it comes to the time of mitigation. The work proposes an early warning solution informed by artificial intelligence to combine climate model predictions and real measurements captured by IoT-based hydrometeorological monitoring systems to better predict the flood, with a higher lead time. The study chose three flood-prone Indian cities; Mumbai, Chennai, and Guwahati, with varied climatic, topographical as well as infrastructural settings. A hybrid deep learning model was trained on past-present floods over 20152024 using Long Short-Term Memory (LSTM) networks to predict time-series flood patterns and Convolutional Neural Networks (CNN) to learn spatial patterns of floods. The sensor measurement of rainfall intensities, water levels and soil saturation were dynamically integrated with the climatic model output of precipitation and sea-level rise. Average lead time reduction of 3.7 hours as compared to the conventional flood forecasting systems was observed with the overall accuracy standing at 91.3 per cent and false alarms cut to 22 per cent, when using the integrated system. The output of the AI in the spatial flood risk maps pinpointed areas of high vulnerability in a way that the evacuation and localization of resources could be focused on those areas. The findings show an AI model can help significantly enhance the accuracy and the timeliness of urban flood early warning systems when coupled with real-time environmental sensing and the climate modeling, or assist in building resilient urban water management and climate response practices.