Senthil M

AI-Driven Urban Resilience: Lessons from Chennai's Smart City Transformation

Cities across the developing world are wrestling with a tough question: how do we make a flood-prone, waterscarce, rapidly growing metropolis more resilient, when your institutions are stretched thin and your budgets are tighter. Chennai, perched on India's southeastern coast, has been asking this question with increasing urgency since catastrophic floods swamped the city in 2015. This paper looks at something relatively unexplored in that context whether artificial intelligence tools, from deep learning flood models to IoT water sensors and NLP-driven citizen engagement platforms, can meaningfully strengthen Chennai's capacity to bounce back from climate shocks. We examined four domains: flood prediction, water governance, infrastructure monitoring, and institutional coordination. Our analysis draws on policy documents, spatial data, field visits and performance data from pilot AI deployments. The picture that emerges is mixed but instructive. Hybrid AI-GIS flood models hit 93% prediction accuracy in trials, and smart water sensors cut leakage losses by 18% in pilot zones. Though there are serious obstacles persist fragmented data systems across agencies, algorithms trained mostly on affluent area data, and a stubborn digital divide that leaves the most vulnerable communities offline. We propose a four tier AI resilience architecture and argue that without deliberate attention to equity and governance reform, AI risks deepening the very disparities resilience planning ought to address.

Assessing Sustainability and Inclusivity in Urban Planning Through Artificial Intelligence: A Case Study of George Town, Chennai

Artificial Intelligence (AI) for Disaster-Resilient Urban Planning: A Comprehensive Data-Driven Review Framework

Rapid urbanization and escalating climate-related risks have significantly heightened the disaster vulnerability of cities worldwide. Traditional urban planning methods, constrained by their reliance on static historical data and reactive strategies, are increasingly inadequate for addressing contemporary hazard complexities. Artificial Intelligence (AI) presents a transformative opportunity for disaster-resilient urban planning through data-driven insights and dynamic decision-making capabilities. This paper evaluates the role of AI in enhancing urban resilience and proposes a conceptual framework grounded in a systematic review of peer reviewed literature and comparative global case studies. The study employs a qualitative review methodology encompassing an examination of academic publications, policy reports, and case studies from disaster prone metropolitan areas. Secondary data were sourced from Scopus, Web of Science, institutional databases, and publicly accessible urban and climate datasets. Results indicate that AI-enabled approaches demonstrate superior capabilities in risk recognition, integrated data analytics, and the formulation of proactive, adaptive planning strategies compared to conventional methods. Comparative case study analysis of Chennai, Rotterdam, Tokyo, and Singapore reveals that the efficacy of AI applications is contingent upon data availability, technological infrastructure, and institutional governance capacity. The study concludes that while AI holds substantial potential to transform disaster-resilient urban design, its effective implementation necessitates robust institutional frameworks, ethical governance, and context-specific adaptation, particularly in developing regions.