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This study aims to enhance fuel moisture estimation using machine learning models and diverse datasets in South Korea.

Dead fuel moisture estimation in mountainous areas of South Korea using satellite data and ground-based observations through a tree-based machine learning approach

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Randomized trial estimates dead fuel moisture in mountainous areas, suggesting improved wildfire monitoring methods.

This study aims to enhance fuel moisture estimation using machine learning models and diverse datasets in South Korea.
Applied tree-based machine learning models, including random forest and extreme gradient boosting.
Input variables consisted of meteorological data, satellite radiance, topography, and temporal features.
Hyper-parameters were optimized using five-fold cross-validation.
Achieved RMSE values of 1.20–1.41% and R2 values of 0.94–0.96 for 10-h FM estimates.
XGB model provided the highest performance with RMSE values of 0.03 to 0.45% for daily and hourly mean FM predictions.
For wildfire-risk thresholds (FM < 10%), models showed an ETS of 0.78 and an accuracy of 0.95.

Kim et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial estimates dead fuel moisture in mountainous areas, suggesting improved wildfire monitoring methods.

This study aims to enhance fuel moisture estimation using machine learning models and diverse datasets in South Korea.
Applied tree-based machine learning models, including random forest and extreme gradient boosting.
Input variables consisted of meteorological data, satellite radiance, topography, and temporal features.
Hyper-parameters were optimized using five-fold cross-validation.
Achieved RMSE values of 1.20–1.41% and R2 values of 0.94–0.96 for 10-h FM estimates.
XGB model provided the highest performance with RMSE values of 0.03 to 0.45% for daily and hourly mean FM predictions.
For wildfire-risk thresholds (FM < 10%), models showed an ETS of 0.78 and an accuracy of 0.95.

Kim et al. (2026) studied this question.

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