## 3 Results and Discussions  
Table 1 presents the flood conditioning factors for the training process that were hybridized by FR and WoE. As can be seen, the FR values ranged between 0 and 1412 for the second class of SPI, while the WoE values varied between \(-37.0\) for the fifth class of SPI and 33.50 for the second class of SPI. The maximum and minimum FR and WoE values were compatible with each other. As SPI values were very high for FR, WoE values of SPI were lower since they were represented by a log-linear approach. The weights of the flood conditioning factors were estimated using the ranking of the W\({}_{\text{j}}\) values that are presented in Table 1 and obtained from the IoE method. A linear scale adopted from [PERSON] [78] was used to determine the pair-wise comparison of the flood conditioning factors. Figure 4 shows the weights of the flood conditioning factors determined using AHP. SPI, TWI, and soil groups were the dominating factors of them all while D\({}_{\text{d}}\) was the least effective on flood hazards. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Factors** & **Class No. ** & **FR** & **W** & **W\({}_{\text{j}}\)** & **Factors** & **Class No. ** & **FR** & **W\({}_{\text{j}}\)** \\ \hline  & 1 & 3.77 & 10.44 & & & 1 & 0 & \(-0.25\) & \\  & 2 & 1.22 & 1.29 & & & 2 & 0 & \(-0.25\) & \\ Elevation & 3 & 0 & 1 & 0.65 & TWI & 3 & 0 & \(-0.25\) & \\ (m) & 4 & 0 & 1 & & & 4 & 0.92 & \(-0.18\) & \\  & 5 & 0 & 1 & & & 5 & 54.02 & 15.13 & \\  & 1 & 6.38 & 10.17 & & & 1 & 0.62 & \(-36.69\) & \\  & 2 & 0.19 & \(-4.14\) & & & 2 & 1412.00 & 33.50 & \\ Slope (\({}^{\circ}\)) & 3 & 0 & \(-4.20\) & 1.21 & SPI & 3 & 1135.58 & 19.30 & 89,778.9 \\  & 4 & 0 & \(-4.20\) & & & 4 & 1011.57 & 9.71 & \\  & 5 & 0 & \(-4.20\) & & & 5 & 0 & \(-37.00\) & \\  & 1 & 18.83 & 15.57 & & & 1 & 0.30 & \(-2.58\) & \\  & 2 & 0.42 & \(-2.08\) & & Curvature & 2 & 1.35 & 4.42 & 0.31 \\  & 3 & 0.76 & \(-0.75\) & & & 3 & 0 & \(-2.65\) & \\  & 4 & 0.29 & \(-2.24\) & & & A & 12.29 & 16.53 & \\ Aspect & 5 & 0.67 & \(-1.31 When the values of the FR and WoE were examined, it was seen that for values of FR \(\geq\) 0, WoE values might be negative. In order to overcome this problem, WoE-WoEmin was adopted for each condition factor in Step 2 of TOPSIS given in Equation (10). Thus, the FR and WoE values became compatible with each other. Thus, as the values of FR and WoE increase, the flood susceptibility also increases, and all the flood conditioning factors are considered as beneficial. For various distance metrics, the closeness coefficients (C\({}_{\text{i}}\)) were computed at 25,000 randomly generated points in the Gokirmak watershed. As the C\({}_{\text{i}}\) values increased, the flood susceptibility throughout the watershed increased. Flood susceptibility maps were generated using various distance metrics by the Krigging interpolation method. Flood susceptibility values were extracted for both training and testing processes for the validation of the predictions. Estimated flood susceptibility maps were validated by the ROC curve method using extracted flood susceptibility values for both training and testing data as given in Figure 5. The AUC values of the training process for FR are 0.898 for Jaccard, 0.902 for Euclidean and Chebyshev, 0.904 for Manhattan, and 0.905 for Soergel while for WoE, they are 0.878 for Jaccard, 0.892 for [PERSON], 0.895 for Euclidean and Manhattan, and 0.896 for [PERSON]. The True Skill Statistics (TSS) values for the training process for FR are 0.643 for [PERSON], 0.653 for [PERSON], and 0.663 for Euclidean, Chebyshev, and Manhattan whereas for WoE, they are 0.633 for [PERSON], 0.643 for [PERSON], 0.653 for Euclidean, and 0.663 for both Manhattan and Chebyshev. The AUC values of the testing process for FR are 0.933 for both [PERSON] and [PERSON], 0.936 for Euclidean, 0.940 for [PERSON], and 0.942 for Manhattan while for WoE, they are 0.926 for [PERSON], 0.932 for [PERSON], 0.935 for Euclidean and Soergel, and 0.941 for Manhattan.