Forest and Land Fire Vulnerability Assessment and Mapping using Machine Learning Method in East Nusa Tenggara Province, Indonesia

Abstract

Forest and land fires are severe disasters for forest ecosystems, diminishing their functionality. Accurate prediction of fire-prone areas aids in effective management and prevention. Machine learning methods have shown promise in this regard. By 2022, East Nusa Tenggara (NTT) had the highest incidence of such fires. This study aims to assess NTT's forest and land fire vulnerability using seven machine learning methods: Gaussian Naive Bayes, Support Vector Machine, Logistic Regression, Artificial Neural Network, Random Forest, Gradient Boosting Machine, and Extreme Gradient Boost. A geospatial dataset integrating NTT's 2022 fire data and fourteen fire-related factors were created using ArcGIS. Feature selection, employing the Information Gain Ratio, identified nine key features: Degree of Slope, Land Cover, NDVI, Annual Rainfall, Distance to Road, Distance to River, Distance to Buildings, Wind Speed, and Solar Radiation. The Random Forest model emerged as optimal, with AUC values of 0.864 and 0.742 for training and testing, respectively. The resulting vulnerability map highlighted factors contributing to NTT's forest fires, including gentle slopes, forest cover, unhealthy vegetation, low rainfall, human activities, remote water access, soil moisture, distant firefighting facilities, low wind speeds, and high solar radiation. Recommendations include land management, fire-resistant vegetation, policy enforcement, community education, and infrastructure enhancement.