Atmospheric Modeling for Wildfire Prediction

Due to climate change, forest regions in California, Western Australia, and Saskatchewan, Canada, are increasingly experiencing severe wildfires, with other climate-related issues affecting the rest of the world. Machine learning (ML) and artificial intelligence (AI) models have emerged to predict wildfire hazards and aid mitigation efforts. However, inconsistencies arise in the wildfire prediction modeling domain due to the database adjustments required to enable complex and real-time modeling. To help address this issue, our paper focuses on creating wildfire prediction models through One-class classification algorithms: Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. Five-fold Cross-Validation was used to validate all One-class ML models to minimize bias in the selection of the training and testing data. These One-class ML models outperformed Two-class ML models using the same ground truth data, with mean accuracy levels between 90 and 99 percent. Shapley values were used to derive the most important features affecting the wildfire prediction model, which is a novel contribution to the field of wildfire prediction. Among the most important factors for models trained on the California data set were the seasonal maximum and mean dew point temperatures. These insights will support mitigation strategies. In providing access to our algorithms, using Python Flask and a web-based tool, the top-performing models were operationalized for deployment as a REST API, with outcomes supporting the potential of our solution for strengthening wildfire mitigation strategies.