Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net)

Climate change-induced heat waves and densely forested areas near urban centers in South Korea create complex challenges for wildfire response systems. Various forest fire models have been developed to address this, each with unique strengths and weaknesses. Process-based models offer high interpretability through human domain knowledge but require extensive optimization, while machine learning models automatically identify important features but have limited interpretability. To leverage the strengths of both models, this study aimed to integrate human domain knowledge into a machine learning framework. IIASA's wildfire cLimate impacts and Adaptation Model (FLAM)—a process-based model incorporating biophysical and human impacts—was developed as a neural network called FLAM-Net. Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. FLAM-Net was applied at multiple scales and integrated through U-Net-based architecture, named deep neural FLAM (DN-FLAM), to produce downscaled predictions. The optimization revealed spatial concentration of fires near metropolitan areas and the east coast, with temporal concentration in spring due to agricultural burning. Integration of multiscale features through DN-FLAM achieved optimal performance with Pearson's r values of 0.943, 0.840, and 0.641 for temporal, spatial, and spatio-temporal validations. Future projections based on shared socioeconomic pathways indicated increasing fire frequencies until 2050, followed by a decrease due to increased precipitation. This study demonstrates the benefits of the hybrid approach, providing interpretability, accuracy, and efficient optimization. These hybrid models offer scientific evidence to guide locally tailored decision-making for climate change-induced forest fires and lay the groundwork for global application through their optimization capabilities.