Collaborative Research: FIRE-WUI: REKINDLE: Resilient Engineering through Knowledge Integration for NetworkeD Lifelines and Ecosystems

Wildfires and their cascading impacts, such as debris flows and flash floods, pose growing threats to communities in the Wildland-Urban Interface (WUI), disrupting critical lifeline infrastructures and amplifying risks to public safety and well-being. This project addresses a national priority by developing a unified, decision-oriented framework that integrates wildfire and compound hazard modeling, infrastructure interdependency analysis, and human adaptive behavior. This approach enables emergency managers, planners, and infrastructure operators to anticipate, prepare for, and recover from complex wildfire-driven disasters, ultimately reducing societal costs and enhancing community resilience. This project has an effective workforce training strategy of providing educational opportunities to learners at several stages: middle school students, high school students, undergraduate students, and graduate students. Internships will be provided with partners in industry and national labs. The project also develops multidisciplinary education modules and tutorials on compound hazard risks. This project advances the science of wildfire resilience by coupling environmental, technological, and social systems within a single, uncertainty-aware model. The project introduces a physics-informed geospatial artificial intelligence (GeoAI) module that fuses Earth observation data with a wildfire knowledge graph to generate probabilistic risk maps for wildfires and related hazards. A multilayer graph machine learning network simulates cascading failures across interdependent lifeline systems, such as power, water, and transportation, under compound hazard scenarios. The project also captures how service disruptions affect community capabilities related to wildland fire response. A bi-level optimization model identifies robust strategies for infrastructure hardening and post-fire recovery, balancing technical performance with economic needs. The research is grounded in robust case studies and co-developed with government, utility, and community stakeholders through a structured engagement process. By integrating artificial intelligence, machine learning, and stakeholder input, this project transforms wildfire resilience planning from fragmented, reactive analysis into a proactive, unified system that supports national efforts to protect lives, infrastructure, and communities. 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.