AI for Disaster Management: Adoption and Challenges

Based on satellite pictures and weather forecasts, artificial intelligence systems may instantly assess floods, building, and road damage, allowing rescuers to more effectively distribute emergency relief, identify those still in danger, and cut off from an escape route.

FREMONT, CA: Floods, fires, hurricanes, and other natural catastrophes endanger more than 160 million people each year. And things are just going to get worse. Natural disasters are already four times more common than they were in 1970. According to projections, the frequency and ferocity of such storms may increase due to climate change. Artificial Intelligence (AI) has the potential to mitigate the damage by more efficiently and effectively allocating relief resources. It has the potential to speed up aid distribution and sharpen the decisions of front-line relief personnel.

Disaster preparedness activities could look considerably different tomorrow than they do today. When a cyclone or hurricane is approaching, geospatial, weather, and historical disaster data can be used to estimate how many people will be displaced and where they would likely relocate. Such information could benefit disaster responders in determining how much aid (water, food, and medical care) is required and where it should be delivered. Based on satellite pictures and weather forecasts, AI systems may instantly assess floods, building, and road damage, allowing rescuers to more effectively distribute emergency relief, identify those still in danger, and cut off from an escape route.

Following disasters, companies are utilizing AI algorithms to evaluate social media feeds. By marking photographs from shelters where people are without blankets or waiting outside in the streets, this type of analysis could reveal crucial on-the-scene information regarding infrastructure damage and relief being delivered to victims.

Nonetheless, despite the efforts of numerous public sector organizations and commercial sector data players to improve disaster assistance, the impact of these efforts is still limited by several obstacles. The first is that the scope is constrained. Many private-sector projects involve one or a few governments or non-governmental organization partners and focus on specialized use cases, typically in isolation from the larger disaster-relief community and without integration into established disaster relief protocols. This fragments efforts and may result in AI-derived insights and algorithmic tools being delivered to firms that cannot sustain them or effectively incorporate them into their decision-making processes.

Second, while there is a wealth of data available that may aid disaster assistance–satellite, geospatial, telecom, social media, and financial–it is not always available when it is needed. Furthermore, datasets are infrequently merged in ways that could yield further information, both with other large datasets and with data from seasoned field operations. This ground view, which is often overlooked and studied methodically, can be even more valuable than big data insights.

Finally, in catastrophic circumstances where human lives are on the line, it's critical to be mindful of AI's limits. Data analysis does not always produce the results that proponents claim, making it difficult to evaluate such claims without a formalized approach for examining algorithm methodologies and assumptions. Commercial buildings, for example, have been subjected to AI models created to assess residential damage, even though these structures employ different materials, construction processes, and standards. There are no norms that developers and consumers have agreed to follow in a future where AI ethics are increasingly scrutinized.

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