## 5 Results
### Results of the Validation Techniques  
The validation method in this research includes two approaches such as reliability and discrimination ability evaluation. AUCROC, precision and efficiency were computed for measuring the discrimination abilities of the models while the reliability was assessed applying MAE and RMSE which were applied in various research works as validation techniques [79; 80; 81]. The present research using effective model-based approaches has produced fifteen LSMs considering both single and combined techniques. Therefore, precision, efficiency, AUC, MAE, and RMSE of 15 models using both of the training and testing sub-set GLIs were computed and presented in Tables 4 and 5 and Figure 10. The prioritization results of the models found best success rate (using training GLIs) of ANN-LR-RF model as it ranked 1 (precision = 0.871, efficiency = 0.878, AUC = 87.83, MAE = 0.016, RMSE = 0.019) through compound factor (CF) analysis. Furthermore, in the case of prediction ability (using testing GLIs), the prioritization results also assigned rank 1 for the ANN-LR-RF model (precision = 0.878, efficiency = 0.893, AUC = 93.98, MAE = 0.117, RMSE = 0.138) which confirmed the best fit of this model in the current research of mapping LSS in Rudraprayag district of Uttarakhand, India. Figure 9: Landslide susceptibility mapping using ensemble of ANN-RF-SVM-LR. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multicolumn{6}{c}{**Training Data Set**} & \multicolumn{6}{c}{**Rank**} & \multirow{2}{*}{**Rank Total**} & \multirow{2}{*}{**CF**} & \multirow{2}{*}{**Priority Rank**} \\ \cline{2-2} \cline{5-12}  & **Precision** & **Efficiency** & **AUC** & **MAE** & **RMSE** & **Precision** & **Efficiency** & **AUC** & **MAE** & **RMSE** & **RMSE** \\ \hline ANN & 0.718 & 0.722 & 85.12 & 0.038 & 0.058 & 9 & 9 & 10 & 3 & 3 & 34 & 6.8 & 4 \\ SVM & 0.695 & 0.703 & 83.39 & 0.096 & 0.156 & 10 & 10 & 14 & 5 & 6 & 45 & 9 & 11 \\ RF & 0.665 & 0.667 & 84.59 & 0.036 & 0.052 & 13 & 13 & 12 & 2 & 2 & 42 & 8.4 & 10 \\ LR & 0.636 & 0.665 & 82.17 & 0.237 & 0.432 & 15 & 15 & 15 & 13 & 15 & 73 & 14.6 & 15 \\ ANN-SVM & 0.678 & 0.687 & 85.07 & 0.365 & 0.107 & 12 & 12 & 11 & 15 & 5 & 55 & 11 & 12 \\ ANN-LR & 0.685 & 0.69 & 85.46 & 0.267 & 0.249 & 11 & 11 & 8 & 14 & 13 & 57 & 11.4 & 13 \\ LR-SVM & 0.663 & 0.683 & 85.84 & 0.182 & 0.266 & 14 & 14 & 6 & 10 & 14 & 58 & 11.6 & 14 \\ LR-RF & 0.857 & 0.871 & 85.48 & 0.206 & 0.245 & 2 & 2 & 7 & 12 & 12 & 35 & 7 & 6 \\ SVM-RF & 0.77 & 0.785 & 85.38 & 0.163 & 0.069 & 7 & 7 & 9 & 7 & 4 & 34 & 6.8 & 5 \\ ANN-RF & 0.775 & 0.789 & 84.01 & 0.093 & 0.168 & 6 & 6 & 13 & 4 & 7 & 36 & 7.2 & 8 \\ ANN-LR-SVM & 0.766 & 0.784 & 86.95 & 0.182 & 0.231 & 8 & 8 & 4 & 9 & 10 & 39 & 7.8 & 9 \\ ANN-RF-SVM & 0.791 & 0.796 & 86.73 & 0.191 & 0.242 & 4 & 4 & 5 & 11 & 11 & 35 & 7 & 7 \\ RF-SVM-LR & 0.846 & 0.861 & 87.38 & 0.165 & 0.215 & 3 & 3 & 3 & 8 & 9 & 26 & 5.2 & 2 \\ ANN-LR-RF & 0.871 & 0.878 & 87.83 & 0.016 & 0.019 & 1 & 1 & 1 & 1 & 1 & 5 & 1 & 1 \\ ANN-RF-SVM-LR & 0.784 & 0.804 & 87.76 & 0.151 & 0.195 & 5 & 5 & 2 & 6 & 8 & 26 & 5.2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of validation techniques and accuracy prioritization based on training datasets.