Abstract

The strong link between climate change and increased wildfire risk suggests a paradigm change on how humans must co-exist with fire and the environment. Different studies have demonstrated that human-induced fire ignitions can account for more than 90 % of forest fires, so human co-existence with wildfires requires informed decision making via preventive policies in order to minimize risk and adapt to new conditions. In this paper, we address the multidimensional effects of three groups of drivers (human activity, geographic and topographic, and land cover) that can be managed to assist in territorial planning under fire risk. We found critical factors of strong interactions with the potential to increase the likelihood of starting a fire. Our solution approach included the application of a Machine Learning method called Random Undersampling and Boosting (RUSBoost) to assess risk (fire occurrence probability), which was subsequently accompanied by a sensitivity analysis that revealed interactions of various levels of risk. The prediction performance of the proposed model was assessed using several statistical measures such as the Receiver Operating Characteristic curve (ROC) and the Area Under the Curve (AUC). The results confirmed the high accuracy of our model, with an AUC of 0.967 and an overall accuracy over test data of 93.01 % after applying a Bayesian approach for hyper-parameter optimization. The study area to test our solution approach comprised the entire geographical territory of central Chile.