Part II: Lessons Learned from Predicting Wildfire Occurrence for CONUS Using Deep Learning and Fire Weather Variables

Abstract

Abstract This paper illustrates the lessons learned as we applied the U-Net3+ deep learning model to the task of building an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range. Through the lens of model performance, we explore the reasons for performance improvements made possible by the model. Lessons include the importance of labeling, the impact of information loss in input variables, and the role of operational considerations in the modeling process. This work offers lessons learned for other interdisciplinary researchers working at the intersection of deep learning and fire occurrence prediction with an eye toward operationalization.