Natural disasters cause significant human, economic, and infrastructural losses worldwide, yet traditional disaster response systems often rely on static risk assessments and isolated data sources that fail to capture evolving severity, spatial dependencies, and real-time operational constraints such as infrastructure...
Floods are common natural disasters that affect people, infrastructure and the availability of essential resources, and identifying affected areas and providing the required support at the right time is challenging. To address this issue, this paper introduces Bharat AI Sentinel, a software-based system designed to sup...
Reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles, and the proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations.
A comprehensive, Artificial Intelligence-driven framework for disaster management is proposed, which synthesizes state-of-the-art computational models with foundational hazard theories and culminates in a translational, highly scalable AI framework engineered for equitable, real-world deployment.
Experimental implementation demonstrates that integrating machine learning with live environmental data significantly improves flood risk assessment while enabling faster dissemination of emergency alerts, making the system suitable for practical disaster management applications in India.
Frequent extreme floods and the limitations of traditional manual consultation—information latency, knowledge fragmentation, and experience-dependent reasoning—create an urgent need for intelligent flood control decision support. To address the complex hydrological conditions and urgent consultation demands involved in...
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