Technology-Driven Disaster Resilience: Developing Predictive and Adaptive Systems for Extreme Events

Adair Morse

Technology-Driven Disaster Resilience: Developing Predictive and Adaptive Systems for Extreme Events

Author(s)	Adair Morse
Country	United States
Abstract	The increasing frequency, intensity, and complexity of extreme events are creating significant challenges for communities, infrastructure, governments, and emergency-management organisations. Floods, cyclones, wildfires, earthquakes, droughts, heatwaves, landslides, and compound hazards can generate cascading impacts that exceed the capacity of conventional disaster-management systems. Advances in artificial intelligence, remote sensing, Internet of Things technologies, geographic information systems, digital twins, satellite communication, edge computing, robotics, and predictive analytics are creating new opportunities for developing technology-driven disaster-resilience systems. This paper examines how emerging technologies can support disaster prediction, early warning, preparedness, response, recovery, and long-term adaptation. A conceptual qualitative methodology is employed to analyse the integration of real-time sensing, geospatial intelligence, artificial intelligence, predictive modelling, communication infrastructure, autonomous systems, and decision-support platforms. The paper proposes an integrated predictive-adaptive resilience framework in which environmental data are continuously collected, analysed, modelled, and translated into risk-informed actions. Particular attention is given to early-warning systems, infrastructure resilience, evacuation planning, emergency communications, disaster-response robotics, digital twins, and community-level resilience. The study also examines challenges associated with data quality, interoperability, cybersecurity, algorithmic bias, technological dependence, digital inequality, and the reliability of AI predictions under unprecedented conditions. The analysis indicates that technology can significantly strengthen disaster resilience when it is integrated with institutional capacity, local knowledge, community participation, robust infrastructure, and clear emergency protocols. The paper concludes that future disaster management should move from reactive response towards predictive, adaptive, and continuously learning resilience systems capable of anticipating hazards, reducing exposure, supporting rapid response, and accelerating recovery.
Keywords	Disaster Resilience, Extreme Events, Artificial Intelligence, Predictive Analytics, Early Warning Systems, Remote Sensing, Digital Twins, Disaster Management, Risk Reduction, Adaptive Systems.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-26

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American Journal of Advanced Multidisciplinary Research and Innovation

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Technology-Driven Disaster Resilience: Developing Predictive and Adaptive Systems for Extreme Events

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