## 4 Results and Discussion
### Comparison of Forest Fire Risk Classes between HFRI and DWI  
In this section, HFRI and DWI distributions by risk class were examined using the results of one year out cross-validation by time and season. Figure 7 shows the classification result of HFRI and DWI for forest fires using machine learning validation data with a two-hour interval from 0:00 to 24:00 KST (local time). The sum of the values in each time zone is 100% and the value for each risk class is the proportion of the forest fire pixels for the corresponding time zone. Both HFRI and DWI produced good performances in the afternoon from 12:00 to 16:00 local time, showing many forest fires occurred in the high risk pixels. However, about 50% and 70% of the forest fires occurred in the early morning and at night (22:00-08:00) had low risk for HFRI and DWI, respectively. This is because both HFRI and DWI are affected by the temporal distribution of forest fires since they are empirical models: samples were not evenly distributed on the temporal domain. Forest fires tend to occur intensively in the afternoon (Supplementary Figure S3), increasing the forest fire risk index. Oppositely, the forest fire risk was low in the early morning and night when there were fewer forest fires. Although a majority of forest fires in the early morning and at night were classified as low class, the actual average number of forest fires classified as low class in the early morning and at night was only up to 58 because the absolute number of forest fires was very small when compared to the afternoon. The largest difference in the proportion between the two indices was observed in low class. For HFRI, the proportion of low class for all times decreased by at least 3.9% up to 26.5% when compared to the DWI. Although both indices rapidly dropped in the morning (6:00-12:00), the rate of Low class was much lower for HFRI than the DWI. While the rate of Moderate and High classes was much larger for HFRI than the DWI at dawn and early morning. It is due to the high relative humidity and very low temperature in the early morning, which play an important role in calculating DWI. The average Rehu of forest fire pixels occurring in the dawn and morning (from 00:00 to 10:00) was 62.4%, which was higher than the average Rehu of 43.4% at the other times (from 11:00 to 23:00). Interestingly, the average values of the Popt  
Figure 7: HFRI and DWI classes by the time with two hours interval. The total sum of each time for each index is 100%, indicating how many forest fires fall into this class as a percentage form. morning were 1683 and 179, respectively, higher than the other times (1295 and 150, respectively), which implies that accessibility increased the risk even at that time. In other words, DWI, simply based on the meteorological conditions, has limitations in simulating the forest fire risk at dawn and early morning, while HFRI improved them by incorporating anthropogenic features in addition to weather conditions. Figure 8 shows the classification results of HFRI and DWI by season. Both HFRI and DWI well simulated the forest fire risk in spring. However, they showed limited performance in summer and fall. DWI did not work well for winter, either. This might be because empirical modeling approaches were used for both indices with the use of much larger sample size in spring than other seasons. The results of the two indices in spring and summer showed almost similar patterns, but very different in fall and winter. In the DWI, the rate of forest fire occurrences in high class in fall and winter rapidly decreased, and most of the forest fires occurred in low class. On the other hand, in HFRI, the proportion of high class was high in fall and winter resulting in a decrease of 30.6 and 30.87% in low class when compared to the DWI. The mean Rehu of forest fire pixels in fall (52%) had the higher value than in spring and winter (40% and 45%), resulting in less prone to forest fires. For these reasons, the percentage of the high class both HFRI and DWI declined in fall.