Assessment of forest fire vulnerability prediction in Indonesia: Seasonal variability analysis using machine learning techniques

Abstract

Forest fires significantly threaten Indonesia’s tropical forests, driven by complex interactions between human activity, environmental conditions and climate variability. This research aims to identify and analyze the factors influencing forest fires in Kalimantan, Sumatra, and Papua during the rainy, dry and all-season conditions using machine learning techniques and create vulnerability prediction maps and categorize risk zones. Eight years (2015–2022) of forest fire data were combined with 15 forest fire susceptible factors that consider of human, environmental, meteorological, and land use/land cover conditioning factors. Random forest (RF) and eXtreme Gradient Boosting (XGB) machine learning models were used to train and validate the dataset through hyperparameter tuning and 10-fold cross-validation for accuracy assessment. The XGB model was selected as the best performer based on accuracy, recall, and F1-score and was used to generate probability values. The evaluation showed that the accuracies and AUC values for the nine models were greater than 0.7, with AUC values ranging from 0.71 to 0.95, indicating good performance. Papua had the highest accuracy, with 90.5%, 91.6%, and 92.5% for all, rainy, and dry seasons, respectively. Population density, elevation, precipitation, soil moisture, NDMI, NDVI, distance from roads and settlements, land surface temperature and peatlands are the key contributing factors of forest fire occurrences. Vulnerability maps categorized into five risk zones, identifying high-risk areas that aligned with observed fire occurrences. This research highlighted the diverse characteristics of factors that determine forest fires and examined their impact on fire occurrences. The findings provide actionable insights for targeted fire management strategies, though future research should incorporate additional variables to improve predictive accuracy and address long-term environmental changes.