NSF Convergence Accelerator – Track D: Artificial Intelligence and Community Driven Wildland Fire Innovation via a WIFIRE Commons Infrastructure for Data and Model Sharing

A century of suppressing wildfires has created a dangerous accumulation of flammable vegetation on landscapes, contributing to megafires that risk human life and property, and permanently destroy ecosystems. Small controllable fires can dramatically reduce the risk of large fires that are uncontrollable. BurnPro3D is a decision support platform to help the fire response and mitigation community understand risks and tradeoffs quickly and accurately to more effectively manage wildfires or conduct controlled burns.

To achieve this vision, this project is developing specific AI innovations to: (i) Use knowledge management techniques to fuse data coming from diverse sources and prepare it for fire modeling; (ii) Conduct physics-based machine learning within next-generation fire models to use deep learning to understand complex processes that drive fire behavior; (iii) Apply constraint optimization methods to address complex tradeoffs in the decision process for the placement and timing of controlled burns; (iv) Employ explainable AI to increase the interpretability of data and models by diverse users all along the decision-making chain.

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.