Machine learning-driven wildfire susceptibility mapping in New South Wales, Australia using remote sensing and explainable artificial intelligence

Rufai Yusuf Zakari (), Owais Ahmed Malik () and Ong Wee-Hong ()
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Rufai Yusuf Zakari: Universiti Brunei Darussalam
Owais Ahmed Malik: Universiti Brunei Darussalam
Ong Wee-Hong: Universiti Brunei Darussalam

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 13, No 12, 15357 pages

Abstract: Abstract Wildfires are among the most devastating environmental disasters threatening the Australian community, causing significant negative impacts on ecosystems and socio-economic activities. This fact suggests the importance of understanding wildfire tendencies, patterns, and vulnerabilities to conserve ecosystems and develop effective prevention and management strategies. In this study, we present a method for generating a dataset of fire events using freely available remote sensing data via Google Earth Engine. Additionally, we evaluate the performance of four machine learning (ML) models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVMs) in developing a wildfire susceptibility map for New South Wales (NSW). These ML techniques were assessed based on 15 independent wildfire-related factors, grouped into four main categories: climate, environment, topography, and socio-economic factors. Six performance metrics were used to compare the predictive performance of the ML algorithms: accuracy, cross-validation accuracy, precision, recall, Kappa, and F1 score. Our results show that XGBoost outperforms all other models, achieving an F1 score of 0.965, a Kappa of 0.937, an accuracy of 0.968, and a cross-validation accuracy of 0.974. Furthermore, the SHapley Additive exPlanations (SHAP) technique was employed to interpret the model’s learning process, revealing that precipitation, drought, soil moisture, and NDVI were the most influential factors. The study not only highlights the probability of fire occurrences across NSW, Australia, but also identifies the key driving factors of wildfires during the 2019–2020 summer season. Local authorities can utilize the wildfire susceptibility map generated in this study for wildfire management and fire suppression activities.

Keywords: Wildfire; New South Wales; Susceptibility mapping; Forest fire; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07395-w

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