###### Abstract  
In the present study, flood hazard susceptibility maps generated using various distance measures in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were analyzed. Widely applied distance measures such as Euclidean, Manhattan, Chebyshev, Jaccard, and Soergel were used in TOPSIS to generate flood hazard susceptibility maps of the Gokirmak sub-basin located in the Western Black Sea Region, Tarkiye. A frequency ratio (FR) and weight of evidence (WoE) were adapted to hybridize the nine flood conditioning factors considered in this study. The Receiver Operating Characteristic (ROC) analysis and Seed Cell Area Index (SCAI) were used for the validation and testing of the generated flood susceptibility maps by extracting 70% and 30% of the inventory data of the generated flood susceptibility map for validation and testing, respectively. When the Area Under Curve (AUC) and SCAI values were examined, it was found that the Manhattan distance metric hybridized with the FR method gave the best prediction results with AUC values of 0.904 and 0.942 for training and testing, respectively. Furthermore, the natural break method was found to give the best predictions of the flood hazard susceptibility classes. So, the Manhattan distance measure could be preferred to Euclidean for flood susceptibility mapping studies. b 2024 14 7023 14 2024 14  
for flood-related studies, enabling time and cost-effective collection and processing massive and detailed geospatial data even in inaccessible regions. RS has been widely used in determining the flood extent [7], monitoring the flood events [8; 9; 10], mapping of the in-undated regions [11; 12], and identifying damages in flood-induced areas [13; 14]. Recent studies on generating flood-related assessments, as aforementioned, are mainly concentrated on using machine learning, bivariate statistics, and multi-criteria decision-making (MCDM) algorithms. Artificial neural networks (ANNs) [15; 16; 17; 18], decision trees (DTs) [19; 20; 21], and support vector machine (SVM) [22; 23; 24] are among the favored machine learning algorithms. Considering the statistical methods in flood susceptibility assessments, the most widely used technique is the bivariate statistical analysis [25; 26; 27], which involves the frequency ratio (FR) method [28; 29; 30], weights of evidence (WoEs) [31; 32], statistical index (SI) [33; 34], certainty factor (CF) [35; 36; 37], belief function (Bel) [38], and index of entropy (IoE) [39]. However, the complex non-linear nature of flooding contradicts the statistical methods because of their dependence on variables obtained from linear assumptions [40; 41; 42]. In flood-related studies, multi-criteria decision-making (MCDM) models have become widely preferred due to their high efficiency in making decisions based on multiple criteria [43; 44]. They are used to delineate flood hazard zonation [45], flood susceptibility mapping [46], flood vulnerability mapping [47], flood risk mapping [48], flash flood analysis [42], and flood forecasting [49]. Regarding the MCDM, the most commonly used techniques are Analytical Hierarchy Process (AHP) [50; 51; 52], VlseKriteri-jumska Optimizacija I Kompromisno Resenje (VIKOR) [53], and Technique for Order of Prioritization by Similarity to Ideal Solution (TOPSIS) [54; 55; 56; 57; 58; 59; 60]. According to the TOPSIS method, the alternatives that yield the shortest distance from the positive ideal solution (PIS) and furthest from the negative ideal solution (NIS) are targeted. Then, the relative risk assessment is achieved by calculating the relative proximity between alternatives and the ideal solutions. However, one major limitation of this method is the lack of a weight calculation module. So, in recent years, other MCDM approaches, such as AHP, have been integrated to retrieve weight values, yielding a hybrid AHP-TOPSIS approach.