A deep learning ensemble model for wildfire susceptibility mapping

Abstract

Devastating wildfires have increased in frequency and intensity over the last few years, worsened by climate change and prolonged droughts. Wildfire susceptibility mapping with machine learning has been proven useful for fire-prevention plans, turning into an indispensable tool in wildfire prevention. However, applications of deep learning models in wildfire susceptibility prediction to date are scarce. This study proposes a new Ensemble model based on two deep learning networks previously presented in literature that achieved remarkable results for forest fire susceptibility and other environmental risks. We compare our model with each of its sub-models, two more deep learning networks, and other machine learning benchmark, namely, XGBoost and SVM. Furthermore, we analyze the effects that different sample patch sizes have on the predictive performance of the algorithms. As case study we selected the fire occurrences in two regions in Chile, from 2013 to 2019. Satellite imagery data for fifteen fire influencing factors in the study area were retrieved to build a dataset to extract the samples to train the models. These factors include elevation, aspect, surface roughness, slope, minimum and maximum temperature, wind speed, precipitation, actual evapotranspiration, climatic water deficit, NDVI, land cover type, distance to rivers, distance to roads and distance to urban areas. During training, the best sample patch size was found to be 25×25 pixels. As a result, the highest area under the curve (AUC) was 0.953 achieved by the Ensemble model, followed by CNN-1 with AUC=0.902. The Ensemble model also achieved the best accuracy, sensitivity, specificity, negative predictive value and F1 score. Finally, the predicted susceptibility maps suggest that static variables can be considered as predisposing factors, while dynamic variables affect the intensity of the predicted probabilities, with an important role of the anthropogenic variables. These resulting maps may be useful to prioritize wildfire surveillance and monitoring in extensive high risk areas.