As urban populations have tripled over the past five decades, the susceptibility of cities to natural disasters like earthquakes and extreme weather has significantly increased, exacerbated by climate change. In response, the United Nations launched the Global Initiative on Resilience to Natural Hazards through AI Solutions at the Barcelona Supercomputing Center. This initiative, building on four years of collaboration among key international organizations, aims to harness artificial intelligence (AI) to enhance disaster management through improved data collection, forecasting accuracy, and communication. AI applications include precise weather forecasting, efficient public alerts, and targeted disaster response efforts. Despite these advancements, challenges such as data quality and the transparency of AI models persist, particularly in vulnerable regions. The initiative seeks to establish responsible AI guidelines and will implement pilot projects in Greece and Georgia to test its recommendations. Additionally, private companies are contributing by bridging data gaps and developing advanced AI models. Another UN program, the Systematic Observations Financing Facility (SOFF), supports poorer countries with funding and technical assistance to enhance their disaster management capabilities. Overall, integrating AI into disaster preparedness and response is pivotal for creating more resilient urban environments in the face of growing climate-related threats.
With urban populations tripling over the last 50 years and climate change intensifying natural disasters, cities face increasing risks from events like earthquakes and extreme weather. In response, the United Nations has unveiled the Global Initiative on Resilience to Natural Hazards through AI Solutions at the Barcelona Supercomputing Center. This new initiative aims to help governments and communities use artificial intelligence (AI) to enhance disaster management.
Building on four years of collaboration among the International Telecommunications Union, World Meteorological Organization (WMO), and U.N. Environment Programme, the initiative focuses on improving data collection, forecasting accuracy, and communication through AI. Monique Kuglitsch, chair of the focus group, highlighted AI’s versatile applications, such as predicting hurricane landfalls and speeding up public alerts by translating warnings into multiple languages swiftly.
AI is also transforming disaster response. Organizations like GiveDirectly have used AI to analyze satellite images and target aid effectively after hurricanes and earthquakes. Meanwhile, private companies are advancing AI climate modeling and earthquake detection. Start-ups like SeismicAI are deploying AI-enhanced sensors for real-time earthquake alerts, and tech giants are developing faster, more efficient forecasting models.
Despite these advancements, challenges remain. AI models depend heavily on quality data, which is often lacking in vulnerable regions. Additionally, complex AI systems can be opaque, making reliability a concern. The UN initiative aims to establish responsible AI guidelines, ensuring transparency and effectiveness across different areas.
Pilot projects under the initiative will test AI applications for wildfire prediction in Greece and improve flood warnings in Georgia. Private firms like Tomorrow.io are also contributing by collecting data from underserved regions to enhance AI models used by cities and major companies.
Another UN effort, the Systematic Observations Financing Facility (SOFF), supports poorer countries with funding and technical aid to bridge data gaps. Johan Stander from the WMO emphasized the need for human oversight alongside AI to ensure reliable disaster management.
As cities grow and climate threats escalate, integrating AI into disaster preparedness and response is crucial. The Global Initiative on Resilience to Natural Hazards through AI Solutions represents a significant step toward safer, more resilient urban environments worldwide.
Sources:
Time
https://time.com/7171445/ai-natural-disaster-cities/ .
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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