Harnessing AI for Spatial Environmental Risk Modeling and Mitigation

Communities across the U.S. face growing risks from extreme weather events such as droughts, wildfires, flash floods, and extreme heat. These events can cause significant economic and social disruptions. This project aims to develop advanced tools to help communities and policymakers better understand local risks and plan for resilience. By leveraging artificial intelligence (AI) and large-scale public data, the project supports informed decision-making to protect people, infrastructure, and economies. The project builds an AI-driven model that integrates large, varied datasets, including spatial data, hazard data, and socioeconomic indicators, to analyze regional environmental risk and vulnerability in the U.S. Utilizing deep learning, ensemble models, and explainable AI methods, the research captures the complex, dynamic structures of environmental risk and community resilience. The project makes these tools publicly available to researchers, planners, and communities to support adaptation strategies in response to extreme weather events. In addition, the project includes mentoring and training for students, preparing them for careers in AI and policy-relevant research, and it supports the NSF’s priority area of AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.