A study on forest fire risk assessment in jiangxi province based on machine learning and geostatistics

Abstract

Jiangxi Province, characterized by abundant forest resources and complex topography, is highly susceptible to forest fires. This study integrated multiple factors, including topography, climate, vegetation, and human activities, and employed machine learning models, specifically random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN), to predict forest fire occurrence in Jiangxi. Using Moderate Resolution Imaging Spectroradiometer L3 fire-point data from 2001–2020, we analyzed the spatiotemporal distribution of forest fires and applied the weight of evidence (WoE) method to evaluate the correlation between forest fires and environmental factors. WoE was employed to select negative samples, which were compared with those obtained using traditional random sampling methods. The optimal model was then utilized to generate seasonal spatial distribution maps of forest fire risk throughout Jiangxi Province. The results showed that over the past two decades, the frequency of forest fires generally decreased. RF demonstrated a significant advantage over SVM and BPNN in predicting forest fires. Vegetation coverage was the most influential factor. In addition, the models trained with WoE-selected negative samples exhibited enhanced accuracy, with area under the curve values increasing from 0.946 to 0.995 for RF, 0.8344 to 0.925 for SVM, and 0.832 to 0.850 for BPNN, compared to those trained with randomly sampled negative data. Finally, forest fires were most frequent during winter, particularly in Ganzhou, Fuzhou, and Ji'an. High-risk fire zones were more dispersed in spring, whereas autumn fires were primarily concentrated in Ganzhou, and fire activity was relatively low during summer. The seasonal forest fire risk maps generated in this study offer valuable insights for guiding forest fire management in the Jiangxi Province and similar regions, providing critical practical significance for informed decision-making.

Keywords

• forest fire
• MODIS
• weight of evidence
• machine learning