## 5 Conclusions  
Existing forest fire risk indices as an operational use are mostly based on weather and fuel data, and often require complex processes through various conditions based on statistics. In this study, a new hourly forest fire risk index (HFRI) based on the Catboost machine learning using various factors including weather, accessibility, and fuel data was proposed over South Korea. HFRI is provided by the ensemble model that linearly combined the integrated and meteorological models with different input variables. HFRI was validated through the ROC curves and AUC values, and compared to the revised FFMC and DWI through the percentile rank. The AUC of HFRI was 0.8434, much higher than the meteorological model (i.e., typical fire risk model). Moreover, HFRI has shown stable performance in identifying forest fire risk areas dynamically with diurnal and seasonal change. In addition, spatial variations exist within administrative districts because of their fine resolution (1 km), which can also help deploy forest fire fighting resources at the district level. However, since HFRI uses local forest density data and the numerical model (i.e., digital forecast model) available in the study area, it is necessary to replace them with globally available data (e.g., Global Data Assimilation and Prediction System) to improve the transferability of the index. In addition, the causes of forest fires may vary by region, it is crucial to extensively train and evaluate the index over multiple areas with different characteristics of forest fires.  
Feature contribution analysis through SHAP values showed that Rehu had the greatest contribution, followed by elevation, Roaddens, and Popdens, in order. Most of the contributing features were related to the accessibility, implying that such features were critical especially for areas with frequent forest fires caused by anthropogenic reasons. Due to these static accessibility variables, the high forest fire risk was generally concentrated around urban areas even though HFRI was complemented through the ensemble model structure with the meteorological model. In the future, improved performance is expected if additional spatiotemporally dynamic accessibility variables, such as the number of floating population and the number of hikers by date, are incorporated in the ensemble model.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/22/8213/s1](http://www.mdpi.com/2076-3417/10/22/8213/s1)]([http://www.mdpi.com/2076-3417/10/22/8213/s1](http://www.mdpi.com/2076-3417/10/22/8213/s1)), Figure S1: SHAP value for elevation data, Figure S2: HFRI (ensemble model) (a-d) and the integrated model(e-h) maps with the four-hour interval, and Figure S3: The forest fire sample distribution by time.  
Conceptualization, Y.K. and J.I.; methodology, Y.K., E.J., and J.I.; validation, Y.K. and E.J.; formal analysis, Y.K. and E.J.; investigation, Y.K. and E.J.; data curation, C.K., and S.K.; writing--original draft preparation, Y.K.; writing--review and editing, E.J., J.I, C.K., and S.K.; visualization, Y.K.; supervision, J.I. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (no.20009742) of Disaster-Safety Industry Promotion Program funded by Ministry of Interior and Safety (MOIS, Korea) and by a grant from the National Institute of Forest Science, Korea, grant number F0500-2018-01. Y.K. was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A3A13075983).  
The authors declare no conflict of interest.