## 4 Discussion  
The harmful effects of disasters in flood-prone areas have amplified in severity over the years. The damage ensuing from these events are also exponentially growing, where serious land use and climate change impacts are alarming. The development of a flood risk map utilizing elevation models, demographics, awareness, and disaster-risk-related data integrated into GIS and analyzed using AHP is an effective tool for evaluating risk. Integrating available data through the archival, automated process, and survey data were possible in deriving each criterion. It is noted in the flood risk map that approximately 83.78% of the total area map is at very low risk, 0.15% is at low, 12.86% at moderate, and 3.26% for high flood risk. 93.92, 0.198, 17.56, and 4.46 square kilometers are very low, low, moderate, and high risk to flood. A small portion of Barangay Rizal and Tumingad are at low risk (yellow-green). Moderate risk was noted on the map covering a part of barangays Rizal, Progresso Este, Progresso Weste, Malilico, Amatong, Anhao, Canduyong, Pato-o, Tumingad, Mayha, Tuburan, and Panique. Then, Gabawan, Batiano, Tabobo-an, and Libertad were at moderate risk covering half of their respective barangay boundaries. Moreover, based on the zoomed-in map of the town proper, the Polacion area, Tulay, Bangon, Anahao, Dapawan, and Poctoy are at high risk to flood occurrence. The possible reason for this is the rapid urbanization and infrastructure development in the town proper of Odiongan, as shown in Figure 11. In addition, moderate to high-risk indexes were observed along the riveranks of Odiongan. In validation, areas at high risk are known to have flood events. The results of the risk maps urge the municipality to plan for flood mitigation or develop a comprehensive flood management plan as a countermeasure during for future flood events. Flood risk assessment is required for the flood management and mitigation of cities and municipalities. However, the specific parameters available in such areas are significantly different, which differs the risk assessment index weight. The study of [PERSON] et al. [15] adopted the frequency of rainstorms and average annual precipitation of counties as hazard parameters, similar to what were used in this study. Average annual rainfall represented 22.59% of the weight of the total hazard parameters which showed its importance or role in identifying hazard level in a certain area. For vulnerability factors, the study considered the following: population density, average area GDP, per capita disposable income, road network density, and land use type. Average income was  
Figure 11: Imagery and flood risk map (50% transparency) with 1:50,000 scale zoomed-in map of town proper of the Municipality of Odiongan. basically identified to have the highest weight among all other factors considered in the vulnerability index with 11.24%, this just shows that the capability to spend affects how people could prepare and protect themselves during a disaster. A risk assessment could also be performed, even in mega-cities, as based on the study of [PERSON] et al. [60]. Parameters such as rainy season, average rainfall, average rainy day for hazard and elevation, slope, river proximity, river density as exposure parameters; and land use, metro line proximity, metro line density, road network proximity and road network density as vulnerability indicators were considered, similarly to what was considered in this study. This shows that methodology used in this study could also be applied in mega-cities here in the Philippines with some revision of factors to be considered which will be based on the city's characteristics. A local study [46] used Confirmatory Factor Analysis (CFA) to simultaneously investigate the interrelationship between vulnerability to natural hazards composed of exposure, sensitivity, and resilience.