Forest Fire Risk Assessment using Machine Learning and Earth Observation Technique in Himalayan Regions: Insights from Rasuwa District, Nepal

Abstract. The complex terrain and increasing forest fire incidents in recent years make high-mountain environments vulnerable to forest fires, posing a threat to ecological and economic well-being. However, research related to forest fires remains limited in the mountain region. The study aimed to assess the forest fire risk by integrating ten predictive layers (biophysical, topographical, climatic, and anthropogenic factors) and a Random Forest (RF) model in Rasuwa, a mountainous district of Nepal. The model was validated using AUC and other statistical metrics. With an AUC of 0.9 and TSS of 0.67, the model shows strong predictive performance and high reliability of the generated fire risk map. The most significant factors in the model's ability to predict the forest fire risk were elevation, temperature, NDVI, and proximity to settlements. The study shows that 29.32 % of Rasuwa district’s forest is at fire risk, with the highest risk in Kalika Rural Municipality (59.4 %). In the study area, approximately 30 % of fire incidents occurred at elevations above 3000 meters. This study emphasizes the potential of the RF algorithms and geospatial methods in similar mountainous regions, aiding the concerned stakeholders and authorities in developing effective forest management plans and improving understanding of forest fire risk.