Artificial Intelligence-driven Framework for Enhancing Climate Resilience in Urban Infrastructure

https://doi.org/10.46610/JoRAIS.2025.v010i03.003

Abstract

Cities are facing growing risks from climate change. Heavy rainfall, flooding, droughts, and heat waves are damaging infrastructure such as drainage lines, power supply systems, and roads. Many of these structures were built decades ago using older climate data and are not designed for present conditions. This leads to frequent service breakdowns and high repair costs. India demonstrates these challenges clearly. Urban growth in cities like Bengaluru, Chennai, and Mumbai has reduced natural drainage and increased stress on existing systems. Monsoon floods now cause traffic delays, power cuts, and waterlogging. Current management methods depend on fixing problems only after failure. This approach is expensive, slow, and leaves communities vulnerable. This study introduces an artificial intelligence (AI) framework to improve urban resilience. The framework uses satellite images, rainfall data, elevation maps, and lives sensor information. Machine learning predicts where failures are likely, while image analysis detects blocked drains or encroachments. The results are presented as risk maps and preventive recommendations. A case study of Bengaluru shows that AI can identify flood-prone zones, test drainage improvements, and guide local authorities in planning. The findings suggest that AI reduces disaster impact, lowers costs, and improves preparedness. The proposed framework also highlights the importance of combining different datasets into one system. The study concludes that AI is not only a technical tool but also a key enabler for sustainable and climate-resilient cities.