## 4 Results and Discussion
### Feature Contribution Based on the Catboost Feature Importances  
Figure 4 shows the feature importance identified by the integrated model based on the Loss function change. Rehu, Roaddens, Elevation, Popdens, and DOY in order were the top five contributing variables identified by Catboost. The most important variable was Rehu, which has been proved as a very useful and relevant variable representing dry weather conditions and dead fuel [20; 60; 61]. Rehu had a clearly opposite relationship with the number of forest fire cases considering their diurnal and seasonal patterns. Rehu has a peak in the dawn and decreases in the afternoon, and has a peak in summer and a decreasing pattern in winter and spring. The Pearson correlation coefficient of Rehu with HFRI was \(-0.8\). Except for Rehu, contributing variables were closely related to accessibility, especially for the Roaddens, Elevation, and Popdens. The Pearson correlation coefficients of the three variables with HFRI were 0.4, \(-0.4\), and 0.3, respectively. This agrees with the fact that most forest fire cases have occurred due to anthropogenic factors. The forest areas with high Roaddens, low Elevation, and high Podens have relatively high accessibility, which is vulnerable to forest fires [27]. DOY moderately contributed to the model. DOY is an effective factor for modeling the seasonality of forest fire risk: frequent forest fires in the dry season (spring and winter), while less in wet summer [62]. Figure 5a depicts the SHAP values of the variables calculated from the integrated model, which shows the degree of the positive or negative effect on the average of the predictions (i.e., the  
Figure 4: The Loss function change feature importance of Catboost machine learning from the integrated model. Figure 3: The one-year-out cross-validation ROC curves. (**a**–**f**) are the one-year-out cross-validation ROC curves for individual years, and (**g**) is the ROC curve for the entire study period. expected values) for all training data. Using the SHAP values, we investigated how each feature contributed to the prediction in the integrated model. This tendency is an overall summary of the SHAP values. It means each sample has a different contribution of each feature for the prediction. For example, when Rehu has a value close to the average rather than a particularly high or low value, the contribution of Roaddens or Elevation to the prediction is much greater. These interactions between features are analyzed with a dependence plot (Figure 5b). SHAP values enable to identify how each feature affects the prediction when the feature has a certain value. In Figure 5a, the higher Rehu has negative contribution to the prediction up to \(-0.2\). On the other hand, when the Rehu was low, it has positive contribution to the prediction up to \(0.13\). The high elevation decreased HFRI. While the low elevation increased the prediction, its effect was smaller than the decreasing effect. When the Roaddens was high, it increased the prediction. When Roaddens was lower, the prediction was lowered. Poptens was inversely related to elevation. It can be inferred that many of the samples predicted as low risk were determined mainly based on the influence of Rehu, elevation, and Roaddens. The weakness of the process of determining the forest fire risk through these three variables is that areas with low Roaddens and high elevation always result in low forest fire risk. It is because Rehu is a dynamic variable, so it reflects the situation at all times, but elevation and Roaddens are static variables. Therefore, it can be seen that the accessibility factors are very important in simulating forest fire risk. These results show the necessity of ensemble the meteorological model to complement the integrated model. Figure 5b shows how Rehu works in the model depending on the Roaddens feature value. The SHAP value was close to 0 when the Rehu value was near 50.