We seek to develop a comprehensive wildfire protection system that guards against current and future catastrophic disasters where critical infrastructure is destroyed and lives are lost. We do this not by responding to emergencies but by supporting strategic planning and policy development where the focus is on reducing the intensity and rate of spread of a wildfire. If this goal is achieved, first responders can safely contain ignitions, minimize damage to infrastructure, and hopefully save lives. We propose to explore the urban edge landscape and infrastructure (often referred to as the wildland urban interface – WUI) to better identify and model the risk of catastrophic wildfires so that more informed planning and policy decisions can be made to inform improved and enhanced design, management and mitigation efforts under current and future climate conditions. Simply put, this research will enhance both energy and climate security against wildfires. We propose key innovations at different scales. First, crowdsourcing, and very high resolution remote sensing for an AI driven fuel model identification. Second, models of wildfire behavior, intensity, spread, informed by downscaled climate change predictions, historic catastrophic wildfires, environmental monitoring. Third, egress models that combine large scale mobile phone data facilitated by data-driven optimization models and computation. To that end we bring expertise from Landscape and Environmental Planning, and Operations Research, brought together through the lens of Complex Systems Science and vast experience in applications of AI.
Figure 1. Sample week of mobile phone data with top origin destinations (left). One hundred years of fires colored by burnt percentage (center). Traffic in morning peak traffic conditions (at 09:00) on a weekday based on the mobile phone data.