## 3 Results and Discussions
For the testing process, the TSS values for FR are 0.756 for all except for [PERSON], which is 0.780. For WoE, they are 0.756 for Euclidean, Chebyshev, and Soergel while they are 0.780 for both Manhattan and Jaccard. Figure 4: Weights of factors with AHP method. the trend of obtaining better results by FR was found to continue in both training and testing processes. The flood susceptibility maps generated by hybridized FR and WoE statistical methods for each distance measure were categorized by classification methods of equal, quantile, natural break (jenks), and geometric intervals in the ArcGIS environment. Figures 6 and 7 show the variations in the percent drainage area of flood susceptibility categories based on the classification algorithms in ArcGIS with distance measure methods hybridized by FR and WoE, respectively. Flood pixels in the classified flood susceptibility potentials were extracted as given in Figures 8 and 9. In accurately estimated flood susceptibility maps, very-low- and low-flood-susceptible pixels are expected to be more than high and very high pixels. Moreover, the flooded points in the classified flood susceptibility maps are expected to be in high and very high categories. The SCAI is the most prominent statistical indicator to put forward this outcome. SCAI is the ratio of values given in Figure 6 to the corresponding values given in Figure 8 for hybridization with FR while for WoE, it is the ratio of values given in Figures 7 and 9 as stated. [PERSON] [21] proposed a power equation to understand the trend of SCAI with regard to the flood potential classes. In the current study, the variations of SCAI values fitted with the flood hazard susceptibility classes were found to be in good agreement with the power equation proposed by [PERSON] [21]. However, the flooded points were not concentrated on very high and high classes, as expected. Although good results were obtained in some classification methods, results showed that the natural break method gave better predictions on flood hazard susceptibility classes, independent of hybridization methods. It was also noted that good results were obtained when other classification methods were used. Figures 10 and 11 show the classification of the flood susceptibility based on the natural break method for various distance methods and hybridization methods. The Manhattan distance measure calculated by the FR hybridized model gave the best results when compared with other metrics considered. Furthermore, the findings obtained with SCAI also confirmed the results of the ROC analysis. Figure 8: Variations in percent flood pixels at flood susceptibility categories based on equal, quantile, jenks, and geometric intervals with distance measure methods hybridized by FR. Figure 7: Variations in percent drainage area of flood susceptibility categories based on equal, quantile, jenks, and geometric intervals with distance measure methods hybridized by WoE. Figure 9: Variations in percent flood pixels at flood susceptibility categories based on equal, quantile, jenks, and geometric intervals with distance measure methods hybridized by WoE. Figure 10: Flood susceptibility maps processed intervals with distance measure methods hybridized by FR using (**a**) Euclidean distance, (**b**) Manhattan distance, (**c**) Chebyshev distance, (**d**) Jaccard distance, (**e**) Soergel distance. In this study, the use of various distance measures was adopted to provide a more comprehensive understanding of the underlying dynamics of the TOPSIS method for generating flood susceptibility maps. Model results were sensitive to both hybridization methods and distance measures. In the literature, the Euclidean distance measure is widely  
Figure 11: Flood susceptibility maps processed intervals with distance measure methods hybridized by WoE using (**a**) Euclidean distance, (**b**) Manhattan distance, (**c**) Chebyshev distance, (**d**) Jaccard distance, (**e**) Soergel distance. used for flood hazard susceptibility mapping studies.