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Journal Articles Environmental Research Letters Year : 2023

Predicting wildfire ignition causes in Southern France using eXplainable Artificial Intelligence (XAI) methods

Abstract

The percentage of wildfires that are ignited by an undetermined origin is substantial in Europe and Mediterranean France. Forest fire experts have recognized the significance of fires with an unknown ignition source since documentation and research of fire causes are important for creating appropriate fire policies and prevention strategies. The use of machine learning in wildfire science has increased considerably and is driven by the increasing availability of large and high-quality datasets. However, the absence of comprehensive fire-cause data hinders the utility of existing fire databases. This study trains and applies a machine-learning based model to classify the cause of fire ignition based on several environmental and anthropogenic features in Southern France using an eXplainable Artificial Intelligence framework. The results demonstrate that the source of unknown caused wildfires can be predicted at various levels of accuracy/natural fires have the highest accuracy (F1-score 0.87) compared to human-caused fires such as accidental (F1-score 0.74) and arson (F1-score 0.64). Factors related to spatiotemporal properties as well as topographic characteristics are considered the most important features in determining the classification of unknown caused fires for the specific area.
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hal-04091177 , version 1 (07-05-2023)

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Christos Bountzouklis, Dennis M Fox, Elena Di Bernardino. Predicting wildfire ignition causes in Southern France using eXplainable Artificial Intelligence (XAI) methods. Environmental Research Letters, 2023, 18 (4), pp.044038. ⟨10.1088/1748-9326/acc8ee⟩. ⟨hal-04091177⟩
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