Not a fragment, but the whole: Map-based evaluation of data-driven Fire Danger Index models

A growing body of literature has focused on predicting wildfire occurrence using machine learning methods, capitalizing on high-resolution data and fire predictors that canonical process-based frameworks largely ignore. Standard evaluation metrics for an ML classifier, while important, provide a potentially limited measure of the model's operational performance for the Fire Danger Index (FDI) forecast. Furthermore, model evaluation is frequently conducted without adequately accounting for false positive rates, despite their critical relevance in operational contexts. In this paper, we revisit the daily FDI model evaluation paradigm and propose a novel method for evaluating a forest fire forecasting model that is aligned with real-world decision-making. Furthermore, we systematically assess performance in accurately predicting fire activity and the false positives (false alarms). We further demonstrate that an ensemble of ML models improves both fire identification and reduces false positives.

OpFML: Pipeline for ML-based Operational Forecasting

Machine learning is finding its application in a multitude of areas in science and research, and Climate and Earth Sciences is no exception to this trend. Operational forecasting systems based on data-driven approaches and machine learning methods deploy models for periodic forecasting. Wildfire danger assessment using machine learning has garnered significant interest in the last decade, as conventional methods often overestimate the risk of wildfires. In this work, we present the code OpFML: Operational Forecasting with Machine Learning. OpFML is a configurable and adaptable pipeline that can be utilized to serve a machine learning model for periodic forecasting. We further demonstrate the capabilities of the pipeline through its application to daily Fire Danger Index forecasting and outline its various features.