Nationwide Daily Wildfire Occurrence Prediction Using Time Proxy Variables and the Canadian Fire Weather Index (FWI)

초록

Climate change has intensified global wildfire risks, yet national-scale prediction remains challenging due to the difficulty of consistently monitoring fuel conditions and human ignition factors. This study introduces calendar-based time proxy variables as structural surrogates for these unobservable drivers and integrates them with the Canadian Fire Weather Index (FWI) within a parsimonious framework for seasonally fire-prone regions such as South Korea. Using 15 years of nationwide wildfire records and daily observations from 100 ASOS stations (2011-2025), predictive performance was evaluated across eight models and five feature sets (Time-only, Weather-only, Weather + Time, FWI-only, and FWI + Time). Based on test-set mean AUC, the Time-only feature set reached 0.7374, clearly exceeding the random-classifier baseline (AUC = 0.5) and indicating the independent predictive value of time proxy variables. Furthermore, integrating time proxies with FWI improved performance, with the best model (CatBoost) achieving test AUC = 0.8394 and Recall = 0.6019. Multi-model SHAP analysis revealed complementary contributions of FWI components (53.7% +/- 4.7%) and time proxy variables (46.3% +/- 4.7%). Overall, the results demonstrate that a simple yet structured input design based on time proxy variables provides meaningful predictive performance for nationwide wildfire early warning systems.

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