An insight into machine-learning algorithms to model human-caused wildfire occurrence

This paper provides insight into the use of Machine Learning (ML) models for the assessment of human-caused wildfire occurrence. It proposes the use of ML within the context of fire risk prediction, and more specifically, in the evaluation of human-induced wildfires in Spain. In this context, three ML algorithms-Random Forest (RF), Boosting Regression Trees (BRT), and Support Vector Machines (SVM)-are implemented and compared with traditional methods like Logistic Regression (LR). Results suggest that the use of any of these ML algorithms leads to an improvement in the accuracy-in terms of the AUC (area under the curve)-of the model when compared to LR outputs. According to the AUC values, RF and BRT seem to be the most adequate methods, reaching AUC values of 0.746 and 0.730 respectively. On the other hand, despite the fact that the SVM yields an AUC value higher than that from LR, the authors consider it inadequate for classifying wildfire occurrences because its calibration is extremely time-consuming.