Random Forest-Based Estimation of 10-h Dead Fuel Moisture Using Automatic Weather System Observations in Gangwon, Republic of Korea

Reliable estimation of 10-hour dead fuel moisture content (10-h DFMC) is essential for forest fire risk forecasting, particularly in mountainous and fire-prone regions such as Gangwon Province, South Korea. This study presents a machine learning model trained on hourly observed 10-h DFMC data from 11 stations during 2024, using routine meteorological observations from Automatic Weather System (AWS) sensors as input. The goal was to estimate 10-h DFMC in real-time using standard operational inputs. The model inputs consisted of 1-h averages of 2 m air temperature, relative humidity, 10 m wind speed, and 1-h accumulated precipitation. As solar radiation data were unavailable, we included Julian day and hour of day (0–23) as proxy variables to partially account for diurnal and seasonal patterns in solar energy input. The observed 10-h DFMC data revealed distinct seasonal and spatial variation: spring and early winter showed persistently low moisture, consistent with peak fire seasons. High-elevation stations retained moisture longer due to snow cover, while coastal sites exhibited greater variability influenced by maritime air masses. The random forest model achieved high predictive accuracy (R² = 0.80; RMSE = 2.73%; MAE = 1.93%) on the test dataset. Station-level evaluation showed R² ranging from 0.76 to 0.86. Relative humidity was the most influential predictor, while precipitation had marginal impact, suggesting that 10-h DFMC is more sensitive to sustained atmospheric humidity than to short-term rainfall. Comparative experiments confirmed that the random forest approach outperformed linear regression and support vector regression and achieved similar performance to gradient boosting. Snow-affected high-altitude sites showed larger errors, indicating the need for future inclusion of snow-state and terrain-related covariates. This study offers a regionally calibrated, operationally feasible model for 10-h DFMC estimation based solely on widely available AWS data. Its structure is inherently transferable to other regions with localized training data, supporting scalable, real-time fire danger assessment systems under a changing climate. This abstract is based on findings from our peer-reviewed article published in December 2025 under the title: “Machine Learning–Based Analysis and Prediction of 10-h Dead Fuel Moisture Content Using Automated Weather Observations in Gangwon Province, South Korea.” This research was funded by the Korea Meteorological Administration Research and Development Program “Advanced Research on Bio- and Agricultural Meteorology” (Grant No. KMA2018-00626).