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Cognitive-Adaptive Warning Systems

Ismatulla KhayrullayevTermez University of Economics and Service, Termez, UzbekistanBarno MatchanovaUrgench State Pedagogical Institute, Urgench, UzbekistanR. N. RavikumarMarwadi University, Rajkot, IndiaS. AarthiMarwadi University, Rajkot, IndiaKhayrulla UrozboevAlfraganus University, Tashkent, UzbekistanMary Subaja ChristoSRM Institute of Science and Technology, Kattankulathur, India

2026ng

ABI

Annotatsiya

The chapter explores the emergence of Cognitive-Adaptive Early Warning Systems (CA-EWS) that integrate Artificial Intelligence and cognitive science to enhance multi-hazard preparedness. Traditional Early Warning Systems emphasize data precision but often neglect how humans perceive and act on alerts. The proposed framework combines machine learning, behavioral analytics, and perception modeling to create adaptive, human-centered warning communication. By leveraging real-time feedback from social media sentiment, mobility data, and community responses, CA-EWS dynamically refines alert clarity, timing, and personalization. Case studies from Japan's AI-enhanced tsunami warning and COVID-19 pandemic systems reveal measurable improvements in public responsiveness, trust, and compliance. The study concludes that embedding cognitive intelligence within EWS transforms reactive systems into proactive, empathetic, and resilient disaster communication ecosystems.

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Mavzular

Seismology and Earthquake Studies Public Relations and Crisis Communication Disaster Management and Resilience

Identifikatorlar

DOI: 10.4018/979-8-3373-7345-4.ch004

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