Wildfire Integrated Modeling, Prediction, and Learning Environment (WIMPLE)

The risk of wildfires has increased significantly in recent years and touched communities not previously at high risk. Effective mitigation of wildfire risk is essential to reduce the potential for catastrophic losses. Accurate assessment of the risk of wildfire on a parcel-by-parcel basis will enable fire departments and homeowners to effectively triage and plan to reduce the risk. We will develop a Wildfire Integrated Modeling, Prediction, and Learning Environment (WIMPLE), a hybrid AI tool for wildfire risk assessment. WIMPLE is based on our Scruff AI framework, which provides integration of different kinds of AI models, sharing and composition of models, with spatiotemporal flexibility in model composition. We will demonstrate WIMPLE by developing a new wildfire risk assessment method that integrates multiple model components such as fire propagation and climate models at different spatial and temporal scales, as well as learning from historical data. We provide a decision-support UI using explainable AI techniques to ensure that predictions and recommendations of WIMPLE can be understood and trusted by users.