Engineering Resilient AI Architectures for Satellite-Assisted Disaster Prediction and Emergency Response: A Systems Approach Inspired by NISAR
DOI:
https://doi.org/10.58445/rars.2923Keywords:
Disaster Resilience, Systems Engineering, Artificial Intelligence, Emergency Management, NISAR Satellite, Multi Agent Systems, Fault Tolerance, Critical Infrastructure Protection, Machine Learning for Disaster Prediction, Geospatial Data Integration, Redundancy Engineering, Reliability Engineering, Machine Learning, Disaster PredictionAbstract
Disaster-resilient infrastructure requires a multidisciplinary approach that integrates systems engineering principles with emerging technologies such as artificial intelligence (AI), satellite-based Earth observation, and multi-agent coordination. This review synthesizes advancements in disaster prediction, preparedness, response, and recovery, with a focus on the integration of AI-driven analytics, fault-tolerant architectures, and redundancy strategies for critical infrastructure protection. Drawing on 87 scholarly and technical sources, the paper examines case studies including NASA-ISRO’s NISAR satellite, edge AI deployments for rapid hazard detection, and multi-agent systems for autonomous disaster recovery. A structured comparative analysis highlights the strengths, limitations, and operational requirements of current approaches. Key findings reveal that combining geospatial intelligence with modular AI architectures enhances both the speed and accuracy of disaster response, while system resilience is maximized through adaptive redundancy and fault-tolerant control. The review concludes with a framework for integrating these technologies into disaster management ecosystems, emphasizing cross-domain data fusion, ethical AI governance, and culturally inclusive communication strategies. This synthesis provides researchers, policymakers, and practitioners with actionable insights for designing next-generation disaster-resilient communities.
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