With hurricane season well underway and with some of my colleagues in the path of a second massive hurricane in the Gulf of Mexico in as many weeks, it’s imperative that we focus on solutions that manage the damage and improve preparedness for health and emergency response resources. AI is leading the way in the areas of predictions and recovery.
NOAA estimates that the average hurricane now causes $22.8 billion in damage. One crucial way to mitigate the destruction is to assess the risk to communities that lie in the path of hurricanes and storm surges as early as possible. AI helps scientists improve the accuracy in estimating where hurricanes are going to make landfall. AI can take in many different types of data, including satellite images, temperature, humidity, etc. and crunching all of that information on GPUs has improved the accuracy of landfall predictions to less than 100 miles in three-day forecasts. By comparison, in the 1980s, three-day forecasts featured an average error of over 400 miles. Speed and accuracy are paramount when it comes to saving lives.
Aside from providing the insights for more accurate hurricane forecasts, AI is at the forefront of emergency preparedness and response. For hurricane recovery, AI-powered applications enable government agencies and civil service organizations to better serve their constituents. Examples of how AI can help include:
Optimizing Resource Allocation: In the midst of an emergency, pressure is on leaders to make wise decisions about how to distribute scarce resources, including emergency personnel and relief supplies. AI can streamline the decision process by running hundreds of scenarios and placing resources where they are needed most.
Identifying Areas of Need: AI's capacity to rapidly analyze vast amounts of data can revolutionize hurricane response efforts. By examining drone footage, AI can quickly identify the most severely damaged areas. This information can then be combined with socioeconomic data, such as median income and food stamp usage, to pinpoint neighborhoods with the greatest need. This automated process can expedite the delivery of aid, saving valuable time compared to traditional manual methods.
Hardening Preparedness: AI can run realistic scenarios and simulate various challenges so that first responders are ready for more situations when disasters occur. Not being surprised by particular conditions means that those scenarios have already been accounted for and plans are already in place and critical resources can be delivered where needed.
Combining AI and human workers when disaster strikes is the future of emergency recovery, but there are still barriers preventing proliferation of this new work paradigm to every state and agency. There cannot be AI without an IA (Information Architecture), and the largest challenge is still data silos. AI needs a flow of trusted, high-quality data and lack of integrated data sharing leads to delays in getting disaster victims the assistance they need. The answer is not necessarily creating a centralized repository where all data resides, as that can become a target for hackers. Federation is a way for organizations to share sensitive and private data without moving it. In this way, AI models could be trained on multiple data sets while no one has to give up control of their data.
The world is at an interesting crossroads where natural disasters are stronger and occurring more frequently, resources are more constrained than ever, and AI is growing in popularity. This intersection represents a giant opportunity for agencies and governments to improve their disaster preparedness and response while also ensuring the equity and privacy of services.
If you are interested in exploring AI for your business, click here to request a meeting with us. Don’t have an information architecture for AI you trust yet? LRS can also help you collect, organize, and analyze your data so that it is business ready. Together, we'll make sure you're prepared for whatever challenges come your way.