Page 14 - Disaster Management: The Standards Perspective

Disaster Management: The Standards Perspective

AI and the Disaster Management Cycle

AI and other advanced technologies can play a pivotal role across the disaster management
cycle: Their deployment greatly improves the capabilities to forecast, handle, and recover from
various disaster types.

1. Mitigation

During this phase, AI and innovative technologies are applied to evaluate risks and project
possible disaster scenarios. Through historical and real-time data from multiple sources
including sensors and satellites are analyzed to detect patterns that may predict natural
hazards like earthquakes, floods, or wildfires. Tools such as Geographic Information
Systems (GIS) and remote sensing data are crucial in identifying high-risk zones which
helps in the formulation of disaster risk reduction (DRR) strategies.
2. Preparedness

In this phase, AI enhances the development of forecasting and monitoring systems.
Predictive analytics utilize data to anticipate disasters, enabling timely warnings and the
efficient mobilization of resources. Technologies like virtual and augmented reality offer
realistic training environments for emergency personnel, improving their preparedness
for actual emergencies.
3. Response

AI coupled with other emerging technologies can support the expediting of actions during
the response phase. Drones, powered by AI, are deployed swiftly to survey damage,
pinpoint survivors, and distribute supplies to hard-to-reach locations. AI-enhanced
analytics optimize the distribution of resources and logistics to ensure rapid assistance.
Tools based on Natural Language Processing (NLP) evaluate real-time data from social
media and other communication streams for up-to-date situational insights; meanwhile,
AI chatbots and virtual assistants support the coordination of response efforts and
information dissemination to the public.

4. Recovery

During the recovery stage, AI is instrumental in assessing damage and supporting the
rebuilding process. Technologies for image recognition analyse structural damages, and
AI-supported decision-making systems facilitate the effective allocation of reconstruction
resources. Data analytics monitor the progress of recovery efforts, highlighting areas
that require additional focus. Emerging technologies like blockchain add a layer of
transparency and security in managing aid distribution.
Overall, the adoption of AI and advanced technologies throughout all phases of disaster
management enhances decision-making and resource management, while strengthening
the community resilience against future disasters.

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