Can Ensemble Techniques and Large-Scale Fire Datasets Improve Predictions of Forest Fire Probability Due to Climate Change?—A Case Study from the Republic of Korea

Abstract

The frequency of forest fires worldwide has increased recently due to climate change, leading to severe and widespread damage. In this study, we investigate potential changes in the fire susceptibility of areas in South Korea arising from climate change. We constructed a dataset of large-scale forest fires from the past decade and employed it in machine learning models that integrate climatic, socioeconomic, and environmental variables to assess the risk of forest fires. According to the results of these models, the eastern region is identified as highly vulnerable to forest fires during the baseline period, while the western region is classified as relatively safe. However, in the future, certain areas along the western coast are predicted to become more susceptible to forest fires. Consequently, as climate change continues, the risk of domestic forest fires is expected to increase, leading to the need for proactive prevention measures and careful management. This study contributes to the understanding of forest fire occurrences under diverse climate scenarios.

Keywords

• forest fire
• climate change
• machine learning
• ensemble
• South Korea