East Kalimantan, the designated site for Indonesia’s new capital, faces rising disaster risks due to rapid urban expansion, deforestation, and ecological degradation. These changes increase the likelihood of multiple, co-occurring hazards—posing serious challenges to sustainable development and disaster management. In response, this study developed a multi-hazard probability assessment using a Random forest algorithm, focusing on four major disasters: floods, landslides, forest fires (FFs), and droughts (DTs). By applying hazard-specific environmental variables, empirical threshold values, and bootstrap-based uncertainty analysis, the study produced spatially differentiated risk maps and identified areas with overlapping vulnerabilities. The results show that high-probability zones for floods and landslides are concentrated in the southern and central regions, while FF and DT risks are more prominent in the northern and western areas. Overlapping hazard zones, though relatively small in spatial extent, highlight critical regions where compound disaster risks may arise. These patterns suggest the need for region-specific mitigation strategies that reflect the dominant hazard characteristics in each area. Overall, the study provides baseline spatial information that can inform land-use planning and disaster management. The identification of environmental thresholds and uncertainty ranges supports more transparent and evidence-based decision-making.

Wildfire activity is intensifying under climate change, particularly in temperate East Asia where human-driven ignitions interact with extreme fire-weather conditions. This study examines wildfire risk during the March 2025 large wildfire event in Korea by applying explainable machine-learning models to assess ignition-prone environments and their spatial relationship with socio-environmental features relevant to exposure and management. CatBoost and LightGBM models were used to estimate wildfire susceptibility based on climatic, topographic, vegetation, and anthropogenic predictors, with SHAP analysis employed to interpret variable contributions. Both models showed strong predictive performance (CatBoost AUC = 0.910; LightGBM AUC = 0.907). Temperature, relative humidity, and wind speed emerged as the dominant climatic drivers, with ignition probability increasing under hot (>25 °C), dry (6 m s⁻¹) conditions. Anthropogenic factors—including proximity to graves, mountain trails, forest roads, and contiguous coniferous stands (≥30 ha)—were consistently associated with elevated ignition likelihood, reflecting the role of human accessibility within pine-dominated landscapes. The socio-environmental overlay analysis further indicated that high-susceptibility zones were spatially aligned with arboreta, private commercial forests, and campsites, highlighting areas where ignition-prone environments coincide with active human use and forest management. These results suggest that wildfire risk in Korea is shaped by the spatial concurrence of climatic extremes, fuel continuity, and socio-environmental exposure. By situating explainable susceptibility modeling within an event-conditioned risk perspective, this study provides practical insights for identifying Wildfire Priority Management Areas (WPMAs) and supporting risk-informed prevention, preparedness, and spatial decision-making under ongoing climate change.

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability.

Anthropogenic ignition sources—such as agricultural residue burning, land clearing, and open waste disposal—are increasingly contributing to wildfire occurrence, particularly in areas undergoing rapid land use change and urban expansion. In South Korea, over 40% of recent wildfires are linked to such activities, with a growing concentration along the urban–rural interface. This study presents a spatially explicit wildfire risk assessment framework that integrates Generalized Additive Models (GAM), Random Forest (RF), and Geographically Weighted Regression (GWR) to identify key environmental and anthropogenic drivers of wildfire ignition. Using a national wildfire database (2001–2025), combined with high-resolution climatic, land-use, and demographic data, the study establishes operational risk thresholds and generates predictive risk maps. Among the models, RF exhibited the highest predictive performance (AUC = 0.82), effectively classifying high- and low-risk areas. GAM revealed complex nonlinear relationships—fire probability increased sharply below 15% relative humidity, peaked at wind speeds around 15 m/s, and was highest at intermediate NDVI values (0.2–0.4). Interestingly, the highest wildfire risks were observed not in remote interiors but along peri-urban zones near Seoul, Incheon, and Daejeon, where land fragmentation, recreational activities, and ignition sources converge. GWR further identified spatial heterogeneity in predictor effects, emphasizing localized risk dynamics. High-risk areas (probability ≥ 0.7) comprised 16.96% of the national territory, especially in eastern Gangwon-do, northern Gyeonggi-do, and parts of Chungcheong and Jeolla provinces. This integrated modeling framework supports region-specific wildfire prevention, land-use planning, and climate-adaptive fire management, and offers a transferable approach for other fire-prone regions.