Predicting Forest Fire Risk and Carbon Pool Vulnerability in the Himalayas: A Machine Learning Approach

The Himalayan region, known for its rich biodiversity and vast carbon sequestration potential, faces increasing threats from forest fires. This study employed four machine learning algorithms i.e., Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Trees (BRT), and Generalized Linear Model (GLM) to predict forest fire susceptibility across five countries (India, Bhutan, Nepal, China (Tibet) and Pakistan in the Himalayas. Twelve key ignition variables, ranging from elevation to wind speed were utilized to develop robust prediction models. The evaluation metrics, including area under the curve (AUC), coefficient of determination (R²), Total Sum of Squares (TSS), Deviance, and SHapley Additive exPlanations (SHAP) values, were used to access the accuracy of the results. The result revealed that RF outperformed other models in distinguishing susceptible areas. The present study also estimated the vulnerable carbon pools (CP) in the region, uncovering significant variability across the study area. The study revealed that India, Bhutan, and Nepal serve as significant carbon reservoirs, underscoring their pivotal role as carbon sinks. Hence, it is imperative to formulate strategies for mitigating or averting forest fires in this region. The findings underscore the urgent need for targeted conservation and management strategies to safeguard this vital ecosystem from escalating forest fire threats. This research provides valuable insights for policymakers and conservationists, striving to preserve the ecological integrity of the Himalayan region.