## 4 Discussion
Additionally, a study [61] developed a comprehensive framework for vulnerability assessment to determine vulnerability that would deliver a transparent understanding and improve community competency leading to the development of methodologies to assess factors and indicators of vulnerability. For this assessment, the combination of hazard, vulnerability, and exposure assessment established a clear definition of risk levels [36; 62]. Compared to other related studies, the evaluation used the Sendai Framework to clear out the true meaning of disaster risk based on all dimensions of vulnerability, exposure, and hazard characteristics of the environment. Regarding hazard assessment, many studies used factors such as topographical [63], natural, and anthropogenic [62] factors. In this study, factors used were average annual rainfall, slope, elevation, soil type, and simulated flood depth, which were decided according to the data availability. In terms of vulnerability assessment, flood vulnerability is affected by factors such as settlement conditions, infrastructure, policy and capacities of the authorities, social inequities, and economic patterns. The study effectively generated a vulnerability map integrating age, gender ratio, average income, individual physical health, educational attainment, emergency preparedness, and types of built-up structures as factors. Exposure assessment of the study is based on the analysis of [64], which used land use and population density as parameters. The study added the additional factor, household number, to justify the flood exposure. The accuracy of generating flood hazard maps is highly dependent on the quality of topographical data [62]. Topographical information such as DEMs is an excellent source to derive topographic factors responsible for flood activity [65]. Although local studies [63; 64] utilized LiDAR-derived DEM due to its inherent high vertical accuracy and resolution. If-SAR DTM from NAMRIA, as one of the highest resolution available DEM in the location, is used in the study, which showed reasonably consistent with the generated maps. The flood risk assessment map demonstrates flood risk areas that must be managed on a priority basis. In some studies, different methodologies were established for assessing flood risk. One uses a hybrid intelligence model [14], probability [66; 67], polygon approach [68], Quantitative risk assessment methods (e.g., Fine Kiney [55] and Maxent model [8]), and MCDA techniques, such as fuzzy majority approach [30], fuzzy variable set theory [44], multi-attribute value, frequency ratio, artificial neural network [65], fuzzy analytical hierarchy process (FAHP) [41], and decision tree [69] to assess the flood risk. Most government units, municipal planners, and other concerned agencies use AHP as an MCA in terms of disaster risk reduction, land use plans, and decisions requiring a comprehensive judgment and recommendation from experts to benefit the community. Using the AHP decision-making method for the multiple flood-related factors is extensively adopted. From the result of the study, it is noticed that AHP proposes a flexible, stepwise, and precise process of analyzing complicated problems in an MCDA environment. In addition, the resolution of complications in multicriteria methods is realized as the primary use of AHP. In this research, where three (3) criteria (hazard, vulnerability, and exposure) with multiple parameters (5, 9, 3, respectively) have different dimensions, it makes a simple MCDA dilemma more complicated. In this study, the AHP-based flood risk assessment method is considered relatively practical, convenient, and promotes interactive usage by flood managers for continuing improvement. AHP is a useful method for selecting contending options in light of a range of objectives to be convened. The computations were not complex, and the MCA did not need to understand the calculation to use the procedure. Nonetheless, the AHP has established considerations because it highlights the knowledge of decision-makers' preferences [32].