Forest Fire Risk Prediction Based on Machine Learning with Shared Socioeconomic Pathways (SSP) Scenarios

Forest fires are one of the major forest disasters that pose various threats to both natural ecosystems and human societies, including biodiversity loss, large-scale destruction of forest resources, greenhouse gas and pollutant emissions, reduced tourism, and weakened ecosystem services. In the Republic of Korea, forest fires are primarily caused by human negligence. However, climate change factors, such as prolonged droughts and changes in precipitation patterns, also play a significant role in increasing the likelihood of forest fire occurrence. This study aimed to develop a machine learning-based forest fire prediction model using anthropogenic activity data, meteorological data, and climate extreme indices derived from SSP scenarios. PyCaret, a low-code machine learning library, was employed to compare and optimize various machine learning algorithms, maximizing predictive performance. The model can be utilized to identify high-risk areas in advance and assess forest fire risks under changing climatic and socioeconomic conditions. Furthermore, it is expected to provide scientific evidence for formulating forest fire prevention and management policies, thereby enhancing disaster response capacity and supporting sustainable forest management.