Publication: Spatial multi-criteria flood hazard assessment and mapping of Timur Laut district, Penang
Date
2022-03-01
Authors
Bunmi, Mudashiru Rofiat
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Abstract
The main goal of this study is to create a model that accurately identifies flood-prone areas in the Northeast Penang area. Three new Multi-criteria Decision-Making (MCDM) models that integrated two groups of flood influencing factors to reduce the number of pairwise comparisons and enable the use of an optimum set of criteria are proposed. Reduced pairwise comparisons can limit uncertainty arising from inconsistency in the decision-making process of a larger number of factors. Three objectives were established to achieve the goal of the study which comprises of comparing the Analytical Hierarchy Process (AHP), triangular fuzzy AHP (TF-AHP), and trapezoidal fuzzy AHP (TZF-AHP) models in developing flood hazard maps, evaluating the influence of each group of factors on the generated flood hazard models, and testing the predicting capability of the models. The thirteen flood influencing factors selected for the flood hazard mapping models were classified into two groups which were the Engineering Factors Group (EFG) consisting of (drainage density, elevation, land use, slope, rainfall, infiltration, topographic wetness index, time of concentration, and flood depth) and the Hazard Factors Group (HFG) consisting of (distance from rivers, distance from roads and buildings, population, and distance from inundated locations). The evaluation results of the AHP, TF-AHP, and TZF-AHP showed that the similarity between the fuzzy AHP methods (TF-AHP, and TZ-AHP) was higher in comparison with the AHP method for flood hazard mapping in this study. The result of the evaluation of the influence of the group of factors on the AHP, TF-AHP, and TZF-AHP flood hazard maps indicated that the HFG had the highest influence on the resulting flood hazard maps for the AHP, TF-AHP, and TZF-AHP models with 40.15%, 42.09%, and 42.33% respectively. Findings also showed the higher conformity score of the HFG FHI score might have contributed to its influence on the flood hazard map and additionally to the weights assigned to the HFG (0.552, 0.47, and 0.43) for AHP, TF-AHP, and TZF-AHP respectively. The findings from the sensitivity analysis showed similar findings with the proposed models affirming the accuracy of the proposed models. The prediction accuracy of the flood hazard maps of the three MCDM models was tested by overlaying the historical flood locations of 1984-2024 affecting 221 locations with the flood hazard maps individually. The findings showed that the flood hazard maps generated by applying EHFG using the AHP, TF-AHP, TZ-AHP models had 100% of the historical flood events occurringin the ‘high’ to ‘very high’ flood hazard class areas respectively. Therefore, it can be concluded that the AHP, TF-AHP, and TZF-AHP models can serve as a reliable tool in the identification of flood hazard areas in this study area and other areas.