Natural disasters have threatened humans as long as they’ve been on the earth. Even though these events are nothing new, the number of recorded disasters has risen sharply in recent decades, mostly due to climate change. In the face of these rising threats, many organizations are turning to artificial intelligence for help.
AI is typically seen as a tool for businesses, but its potential goes far beyond making companies more efficient. The technology’s data analysis and predictive capabilities make it ideal for informing safer, more effective disaster responses. Here are ten examples of how AI helps in the disaster recovery process.
1. Predicting Disasters Before They Happen
With enough data, AI applications can predict future events with remarkable accuracy, and scientists have a wealth of historical information about natural disasters. Using this data, AI can predict future events like hurricanes and floods. When an area can see that these storms are coming, they can take the necessary steps to prepare, lessening the damage.
2. Assessing Disaster Recovery Strategies
Many businesses and organizations have disaster preparedness plans in case of an emergency but won’t know how effective they are until an actual disaster. AI is changing that, as it can analyze these plans and assess if they have any weak points. Companies already use AI like this to find operational inefficiencies, and the same process can highlight the strengths and weaknesses of disaster response strategies.
3. Identifying Needed Aid
When AI programs look at past events, they can see how they affect people and apply this to future disasters. They could predict things like how many people would be displaced, giving authorities a better understanding of potential needs. Response teams could then allocate the necessary resources ahead of time, enabling quicker, more effective responses.
4. Helping Response Teams Navigate
During or after a disaster, rescue teams need to navigate the area quickly to find and help victims. AI can analyze data like flooding and road damage and give these teams real-time updates on the best route to take. These readings could help first responders stay safe or find areas that could hold people in more urgent need.
5. Evaluating Building Damage
After a disaster, AI can help assess infrastructure damage, both to indicate potential risks and estimate repair costs. Since storms accounted for more than $4 billion in damage in 2015 alone, these estimates are crucial. Cities and organizations can understand their restoration needs more accurately and prepare appropriately.
6. Determining Necessary Funding
Similarly, AI can calculate the cost for disaster recovery as a whole, not just building and infrastructure repair. By analyzing damage, displacement and similar situations in the past, they could present an estimated budget for recovery. Areas would then know how much to allocate, raise or ask for in the aftermath of a disaster.
7. Enabling Clear Communication
Natural disasters can create a lot of confusion, simultaneously making communication more urgent and more challenging. AI systems can field questions like automated customer service chatbots, providing quick answers and understanding people’s needs. If someone needs to speak with another person, these programs can determine the most appropriate authority for each issue and connect them.
8. Improving Warning Systems
Fast and clear warnings can save lives in a disaster, but managing a city or even country-wide system is challenging. AI can automate the process, assessing the situation in real-time, and sending appropriate warnings to those who need them. Chile used such an AI system in 2015, texting coastal residents to evacuate after an earthquake to avoid a tsunami.
9. Raising Awareness for Areas in Need
Some natural disasters are so destructive that areas can’t recover on their own. In these situations, AI can promote areas’ need, promoting related social media posts to raise awareness. They can then reach more people than a traditional approach, leading to more help.
10. Informing Future Actions
One of the most beneficial aspects of AI is its ability to learn from past mistakes and highlight ways forward. After a disaster, machine learning systems can determine which efforts were effective and which weren’t and produce suggestions for future actions. Areas could then better prepare themselves better against future events.
AI in Disaster Recovery Can Save Lives
AI may not be able to stop disasters, but it can inform our actions before, during and after them. Just as these technologies help businesses become more efficient, they can help disaster-prone areas become safer. With more advancement and adoption, AI could dramatically reduce disaster-related injuries and deaths.
