AI and AOIs: Predicting Fire Risk with Remote Sensing and Field Data

The unpredictable nature of the start and spread of fires prove them to be a prominent threat to cities across the United States. It is especially challenging to predict fires due to both the environment and humans acting as contributing factors to their emergence. Limited detailed knowledge on fire potential has led to poor resource allocation by cities, contributing to existing fires growing to uncontainable levels. This problem can be addressed using a holistic method to evaluate the likelihood of wildfires. Our goal was to create FIRECAST: a Fire Index Risk Estimator using Climate, Anthropogenic, and Soil Trends. This exploratory tool utilizes machine learning to provide timely risk assessments to evaluate a region’s susceptibility to wildfires. For inputs, we used data from the Global Human Settlement Layer, Normalized Difference Vegetation Index and Water Deficit from MODIS, as well as soil moisture, maximum temperature, precipitation, and wind speed. Furthermore, we utilized images of our local community collected through the GLOBE Observer App to visually validate land cover conditions and enhance the spatial relevance of our model inputs. We then preprocessed the data to ensure consistency and handle missing values. In order to train FIRECAST, we used XGBoost, a Gradient Boosting model known for its ability to handle nonlinear environmental data. Finally we used mean absolute error to evaluate its effectiveness. FIRECAST aims to provide a near on-demand wildfire risk calculation to aid local communities in resource allocation and preventative measures. It is clear that humans are a risk factor for wildfire potential and must be included in risk assessments. FIRECAST would provide authorities with more frequent risk estimations that take into account both environmental and anthropogenic factors. In the future, expanding input dataset variety to include more social factors, such as population density and even cultural fire-related practices, as well as historic wildfire data, would improve the accuracy of FIRECAST and ensure greater consistency with established indices including the FEMA National Risk Index and USGS Wildland Fire Potential Index. Improvements to FIRECAST will continue to further our objective of providing superior risk assessments to prevent and protect against wildfires.