**5. Results**

The weights from the GeoDIV model are summarized in Table 4 and Figure 5. Among the conditioning factors that eventually remained in the model, the highest factor-level weight was obtained from slope angle (q value of 0.264). It was followed by proximity to roads, land use/cover and proximity to faults (q values of 0.174, 0.151 and 0.147, respectively). For these factors, the classes with the highest class-level weights were the

"greater than 18 degrees" (IV = 2.01) for slope angle, "0 to 285 m" (IV = 1.16) for proximity to roads, "scrub vegetation" (IV = 1.06) for land use/cover and "0–1290 m" (IV = 0.72) for proximity to faults. The rest of conditioning factors were found to have much lower factor-level weights (q values below 0.10). Plan curvature was the factor with the lowest weight (q value of 0.016).

According to the correlations between Tables 3 and 5, the impact degree and types of the different pairwise interactions of factors were determined. The interaction between slope angle and proximity to roads presented the highest weight value (q value of 0.488). This value was greater than the sum of their individual values, indicating that their interaction type was nonlinearly enhanced. Generally, the weights of all the factors (even the lowest of plan curvature) were significantly increased by slope angle, achieving either nonlinear enhancement or bivariate enhancement.

The LS map from GeoDIV model is illustrated in Figure 6. It shows that the "high" and "very high" susceptibility zones are mainly in the southern and northern parts of the vicinity of Pinios artificial lake, with some large pockets of "high" susceptibility in the western part. These two zones cover 25% and 12%of the lake's vicinity, respectively.

## *Validation and Comparison*

In order to evaluate the performance of a model applied for LS assessment and mapping, a validation step is required. Since it can provide information about the accuracy and prediction ability of the model, and thus the reliability of its LS output, this step is crucial for any relevant research effort. A standard validation procedure is one based on success and prediction rates [28,45,48]. This specific procedure depends on the creation of two rate curves explaining the percentages of landslides that fall into defined LS ranks. These curves are graphically presented in cumulative frequency diagrams, with respect to the two different datasets of landslide inventory. For the success rate curve, the landslide training dataset was used to indicate how well the model fits to the training data. On the contrary, for the prediction rate curve, the "independent" landslide validation dataset was used to show how well the model can predict the distribution of future landslides [57].

To obtain the success and prediction rate curves in this study, the overall LS score (Equation (3)) was initially sorted in descending order (from high to low). Then, the ordered LS score was divided into 100 classes with 1% cumulative intervals. The resultant LS ranks (0–100%, where a higher rank means a lower LS score) were plotted on the x-axis, whereas their cumulative percentages of training and validation landslide data are on the y-axis. An area under curve (AUC) value was eventually calculated for each of the two rate curves indicating the accuracy and prediction ability of GeoDIV model, respectively. With a range of 0.5–1.0, this value reflects the model's performance.

Aiming to confirm the potential "superiority" of the hybrid modeling against the individual modeling and explore the impact of GeoDetector-based factor selection on LS assessment, the individual IV model was also applied, and its validation results were compared with those of GeoDIV model. In this context, IVs were additionally calculated for the classes of statistically insignificant factors (not included in GeoDIV model). The overall LS score (presented also by classes, in Figures 7 and 8) was then obtained by the summation of all the fourteen IV-weighted factors as follows:

$$LS = \sum\_{j=1}^{n} IV\_j \tag{5}$$

**Figure 7.** The landslide susceptibility map produced by the individual IV model.

**Figure 8.** Diagrams with the coverage area percentages of the landslide susceptibility classes for the models: (**a**) hybrid GeoDIV model; (**b**) individual IV model.

Based on the *LS* score rank, the success and prediction rate curves were created, and the relative AUC values were calculated for individual *IV* model as well.

The results of validation procedure for both models are presented in Figure 9. The success and prediction rate curves indicate that the first 30% of the LS ranks derived from GeoDIV model can explain about 70% of landslide training data and 60% of landslide validation data, respectively. Moreover, the relevant AUC values of 0.78 and 0.76 revealed remarkable accuracy (data fitting) and prediction ability of the model. All these results were found to be worse for individual IV model, with explanation percentages of 60% and 50%, respectively, and AUC values of 0.72 and 0.71, respectively.

**Figure 9.** The results of the validation procedure: (**a**) success rate curves; (**b**) prediction rate curves.
