## 5 Results and Analysis
### Model Validation and Comparision  
Validation of the models was done using various statistical indices (Table 2 and Figures 4 and 5). In terms of the training performance, the RSSLR model, which achieved the greatest values of NPV (82.28%), SST (84.95%), ACC (80%), and TN (65%) indices, ranked as the best model; followed by the BLR and CCLR models with NPV (79.75%), SST (83.33%), ACC (79.44%), ACC (79.44%), and TN (63%); followed by the LR model with NPV (75.95%), SST (80.381%), ACC (77.78%), and TN (60%); and the DLR model with NPV (69.62%), SST (76.47%), ACC (73.89%), and TN (55%), respectively. In terms of the TP and PPV indices, the BLR, CGLR, and LR models were better with 80% and 79.21%; followed by the RSSLR model with 79% and 78.22%; and the RLR model with 78% and 77.23%. For the FP index, the DLR model was better with 23%; followed by the RSSLR model with 22%; and the BLR, CGLR, and RL models with 21%. The DLR model was better in terms of the FN index (24%), followed by the LR model with 19%, the BLR and CGLR models with 16%, and the RSSLR model with 14%, respectively. For the SPF index, the BLR and CGLR models were better with 75%, followed by the RSSLR, LR, and DLR models with 74.71%, 74.07%, and 70.51%, respectively. In terms of the Kappa index, the RSSLR model performance with 0.5984 was the best, followed by the BLR, CGLR, LR, and DLR models with 0.586, 0.5855, 0.5501, and 0.469, respectively (Table 2). In terms of the validation performance, RSSLR was better with TP = 32%, followed by DLR, BLR, and CGLR and LR with 31%, 30%, and 29%, respectively. For the TN index, DLR had the best performance with 25%, followed by the BLR (23%), CGLR and LR (22%), and RSSLR (21%) models. In terms of the FP index, the CGLR and LR models were better with 14%, followed by the BLR, and RSSLR models with 13%, 12%, and 11%, respectively. For the FN index, the RSSLR model was more precise with 12%, followed by the CGLR and LR (11%), BLR (10%), and DLR (8%) models. In terms of the PPV index, RSSLR was better with 74.42%, followed by the DLR, BLR, and CGLR and LR models with 72.09%, 69.77%, and 67.44% respectively. For the NPV index, DLR was better with 75.76%, followed by BLR (69.70%), CGLR (66.67%), LR (66.67%), and RSSLR (63.64%). For the SST index, DLR was better with 79.49%, followed by the BLR (75%), RSSLR (72.73%), and CGLR and LR (72.50%) models. In terms of the SPF index, DLR was better with 67.57%, followed by RSSLR with 65.63%, BLR with 63.89%, and CGLR and LR with 61.11%. In terms of the ACC and Kappa indices, DLR was more effective with 73.68% and 0.472, followed by BLR with 69.74% and 0.391, LR with 67.11% and 0.3375, and CGLR with 67.11% and 0.338 (Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Factor** & **Average Merit (AM)** \\ \hline
1 & Rainfall & 76.111 \\
2 & Land use & 72.222 \\
3 & Elevation & 68.333 \\
4 & STI & 61.111 \\
5 & Soil & 60 \\
6 & Slope & 59.444 \\
7 & TWI & 56.111 \\
8 & Curvature & 53.889 \\
9 & Flow direction & 53.889 \\
10 & River density & 52.222 \\
11 & Geology & 49.444 \\
12 & Aspect & 44.444 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factor ranks extracted using the OneR feature selection method. RMSE was also used to assess the accuracy of the models. Figure 4 shows the RMSE value; the BLR model was better for training data with RMSE = 0.3681, then LR (RMSE = 0.3687), CGLR (RMSE = 0.3692), RSSLR (RMSE = 0.3962), and DLR (RMSE = 0.3954), respectively. For the validation data, DLR was better with RMSE = 0.4442, followed by RSSLR (RMSE = 0.4479), BLR (RMSE = 0.4772), CGLR (RMSE = 0.4895), and LR (RMSE = 0.49), respectively. In addition, the ROC method was used to compare the performance of the developed models (Figure 5).