## 5 Results
### Application of KLR, RF, and ADTree Models  
Figure 5 illustrates the spring potential map constructed by the KLR method. Based on the visual inspection of the produced spring potential maps, the occurrence of spring appears to follow the spatial distribution of elevation and the factor distance to streams. The high and very high potential groundwater spring zones cover mainly the central and north areas, whereas the south area exhibits low to very low values. The high spring potential class was estimated to cover 5.02% of the study area, whereas low and very low spring potential classes cover 77.71% of the area (Table 4).  
Figure 5: Groundwater spring potential map by the KLR model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Explanatory Factors** & **Classes** & **No. of Pixels in Domain** & **No. of Springs** & **FR** \\ \hline \multirow{4}{*}{Soil} & Calcari-Gypsiric Arenosols (Arc) & 67,657 & 10 & 0.487 \\  & Haptic Arenosols (ARh) & 17,325 & 10 & 1.902 \\  & Calcareous red clay (CMe) & 8418 & 0 & 0.000 \\  & Luvi-Calcic Kastanozems (KSk) & 58,154 & 26 & 1.473 \\ \hline \multirow{4}{*}{Land use} & Farmland & 33,485 & 10 & 0.984 \\  & Forest & 5148 & 0 & 0.000 \\  & Grass & 89,545 & 30 & 1.104 \\ \cline{1-1}  & Water & 743 & 0 & 0.000 \\ \cline{1-1}  & Residential areas & 13,036 & 2 & 0.505 \\ \cline{1-1}  & Others & 9,597 & 4 & 1.373 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._To enhance the performance of the RF method, a tuning process that is based on the grid search method was necessary [74]. The results of the tuning process indicated the optimal parameters to be for ntree 1500 trees and for the mtry parameter of 11. The implementation of RF also provided some extra information concerning the importance of the spring explanatory factors on the overall spring potential mapping. This was achieved by calculating the mean decrease accuracy and the mean decrease Gini [75] (Figure 6). Higher values for both measures indicate that the factor is relatively more significant [76]. According, to those two metrics, the most important factor was lithology, followed by elevation.  
Figure 7 illustrates the groundwater spring potential map constructed by the RF model. Based on the visual inspection of the spring potential map, it could be concluded that spring occurrence follows in this case the spatial pattern of the stream network, with high and very high potential zones covering mainly the central area, whereas the south area illustrates low to very low values. The high spring potential class covers 7.01% of the area, whereas low and very low spring potential classes cover 68.47% of the area (Table 4).  
Figure 8 shows the spring potential map constructed by the ADTree model based on the natural break method [77; 78]. Compared to the previous methods, the ADTree method provides a rather different spatial distribution. The high spring potential class covers 9.15% of the area, whereas low and very low spring potential classes cover 82.56% of the area. It seems that the ADTree method distinguishes with clarity the potential nonspring and spring areas compared to the other two methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **KLR (\%)** & **RF (\%)** & **ADTree (\%)** \\ \hline Very low & 58.80 & 46.90 & 76.66 \\ Low & 18.91 & 21.57 & 5.90 \\ Moderate & 10.10 & 13.77 & 4.04 \\ High & 7.18 & 10.76 & 4.25 \\ Very High & 5.02 & 7.01 & 9.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentages of groundwater spring-potential classes.  
Figure 6: Mean decrease accuracy and mean decrease Gini (Streams: Distance to streams; Roads: Distance to roads; Plane: Plan curvature; Profile: Profile curvature).  
Figure 8: Groundwater spring potential map by the ADTree model.  
Figure 7: Groundwater spring potential map by the RF model.