Wildfire Risk Assessment in a Restricted Military–Civilian In-terface: A Multi-Model Analytical Framework from the Korean DMZ

Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow non-linear thresholds—relative humidity at 13.8–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spa-tial heterogeneity, with military proximity exerting stronger influence in eastern and northern sectors, while meteorological effects varied geographically. Based on these out-puts, a unified analytical framework was established in which RF-derived ignition proba-bilities are interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable meth-od for quantifying ignition risk in politically sensitive, access-restricted landscapes, offer-ing valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk.

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