## 3 Results
### Importance and Contribution of Landslide Influencing Factors (LIFs)  
The independent importance of an influencing factor is one of the most significant analyses in ML. The role of the influencing factors and their contribution to modeling plays a vital role in achieving reliable results. In this study, the factor importance in regard to all of the five models was first assessed using the varImp function of the 'caret' package. This function calculates and documents the mean decrease (scaled) of accuracy in 'class-specific' manners. The details of the contribution of these factors in each model are shown in Figure 7a. It can be observed in Figure 7a that during modeling through the RF and GBM approaches, LCLU was the most significant contributing factor. On the other hand, elevation took the lead in the SVM and LR models, and aspect was the most significant contributing factor in the maxENT model. The varying nature of the factor contribution in the different models reflects the working behavior of these models. Therefore, keeping in view the contribution of these factors, each model provided different results in terms of prediction (Figure 8).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Statistical Definition** & **Usage** \\ \hline Precision & \(\frac{\text{TP}}{\text{(TP+FP)}}\) & This evaluates the fraction of TP samples among all predicted positive samples. \\ \hline Recall & \(\frac{\text{TP}}{\text{TP+FN}}\) & This quantifies the fraction of TP samples among all real positive samples in the data. \\ \hline F1 score & \(\frac{2*\text{PPV+SST}}{\text{PPV+SST}}\) & This harmonic mean of precision and recall provides a value between 0 (worst) and 1 (best). \\ \hline ACC & \(\frac{\text{(TP+TN)}}{\text{(TP+TN+FP+FN)}}\) & Accuracy is the quantification of percentage samples for accurately predicted data in inventory/catalogue. \\ \hline MCC & \(\frac{\text{TP+TN}-\text{FP+FN}}{\sqrt{\text{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}}\) & The most comprehensive index provides values between \(-1\) (disagreement between sample and prediction) and 1 (as a perfect prediction with respect to samples). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic details of index-based evaluation measures.  
Figure 7: LIF importance analysis: (**a**) contribution to each model, (**b**) relative importance and overall contribution.  
However, considering this inconsistency of the factors contributing to each model, the need to assess the relative importance and overall contribution of these incorporated factors was important. Therefore, in this study, we also incorporated a technique presented by [PERSON] et al. [66], which works on the principle of RF to assess the importance of factors using MeanDecreaseAccuracy and MeanDecreaseGini in the 'caret' and 'randomForest' libraries in R. MeanDecreaseAccuracy rescales the class-specific importance scores using  
Figure 8: Landslide susceptibility maps: (**a**) RF, (**b**) SVM, (**c**) maxent, (**d**) GBM, and (**e**) LR.  
'standard error', employing their mean. On the other hand, MeanDecreaseGini works on the principle of splitting a variable through the average gain, which works on a local decision function to select the best split. This importance analysis counts the number of observations against each factor in the data. The importance of each factor calculated through this approach is shown in Figure 7b. The results showed that LCLU, NDVI, elevation, slope and aspect, distance to drainage, distance to road, and distance to faults exhibited high importance for landslide susceptibility, compared with the other predictors such as rainfall, geology, TPI, and TWI in the area. This ranking and importance contribution analysis agrees with the performances of the models in terms of their accuracies, as described in the performance evaluation section.