article · Ain Shams Engineering Journal
Flooding represents one of the most frequent global natural hazards. In Kakegawa, Japan, flood susceptibility maps were produced and evaluated using two distinct spatial modelling approaches within a geographic information system: the analytical hierarchy process and the frequency ratio model. The investigation examined eleven flood-causing factors against a spatial dataset of one hundred flood locations, dividing them into seventy training points and thirty validation points. Assessment through receiver operating characteristic analysis demonstrated that both techniques produced viable predictions. The analytical hierarchy process achieved an area under the curve value of 85.5 percent, outperforming the frequency ratio approach, which recorded 67 percent. The superior accuracy of the hierarchical process stemmed from the integration of expert judgements, whereas the frequency ratio method was constrained by its reliance on basic arithmetic calculations.
Accurate mapping of flood-prone zones is essential for disaster preparedness and urban resilience. Comparing analytical models clarifies how relying on expert judgements versus automated statistical counts affects predictive accuracy. These insights assist local authorities and emergency planners in selecting reliable assessment tools to pinpoint vulnerable sites before severe weather strikes.
The work presents an applied spatial assessment tool that decision-makers and municipal planners can use to identify flood-prone areas. Because the methodology was tested directly on real-world geographical data in Kakegawa, it is an applied framework that could be integrated into spatial planning software or civil protection consulting workflows. However, deploying it in new regions requires acquiring local terrain datasets and expert input.
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Floods are one of the most common catastrophes in the world. This study generates the flood susceptibility maps (FSM) using AHP and FR in Kakegawa, Japan. A set of 100 flood points were presented in an ArcGIS environment where 70 points were chosen at random as a training dataset while 30 ones were used for validation. Eleven flood causative factors were calculated and utilized to generate the flood vulnerability maps. For the validation 30% data sub-sample set, FSM was completed by creating the receiver operating characteristic curve and the area under the curve (AUC). The results indicate that the two methods show sensible accuracy since the AUC for FR and AHP are 67% and 85.5% respectively. AHP showed higher accuracy due to the expert opinion that being shared while FR achieved lower precision because of its simple arithmetic procedures. The results help decision-makers in determining the locations vulnerable to flooding.
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DOI: 10.1016/j.asej.2023.102453
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