Comparison of the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Logic for Flood Exposure Risk Assessment in Arid Regions

Abstract

Flood risk assessment is an important tool for urban planning, land development, and hydrological analysis. The flood risks are very high in arid countries due to the nature of the rainfall resulting from thunderstorms and the land cover, which comprises mostly very dry arid soil. Several methods have been used to assess the flood risk, depending on various factors that affect the likelihood of occurrence. However, the selection of these factors and the weight assigned to them remain rather arbitrary. This study assesses the risk of flood occurrence in arid regions based on land cover, soil type, precipitation, elevation, and flow accumulation. Thematic maps of the aforementioned factors for the study area were prepared using GIS. The Fuzzy Analytic Hierarchy Process (F-AHP) was used to calculate the likelihood of the flood occurrence, and land use was used to assess the exposure impact. Using the likelihood map (i.e., probability) from the Fuzzy-AHP and an exposure map, the flood risk was assessed. This method was applied to Qatar as a case study. Results were compared with those produced by fuzzy logic. To explore the pairwise importance of the F-AHP, equal weight analysis was performed. The resulting risk map shows that the majority of urbanized areas in Qatar are within the high-risk zone, with some smaller parts within the very high flood-risk area. The majority of the country is within the low-risk zone. Some areas, especially land depressions, are located within the intermediate-risk category. Comparison of Fuzzy logic and the F-AHP showed that both have similarities in the low-risk and differences in the high-risk zones. This reveals that the F-AHP is probably more accurate than other methods as it accounts for higher variability.

Hybrid Fuzzy AHP and Frequency Ratio Methods for Assessing Flood Susceptibility in Bayech Basin, Southwestern Tunisia

“…It is a multicriteria method commonly used for qualitative and quantitative analysis of a hierarchical list of the most influencing factors to get insights on the decisionmaking process. The presence of fuzziness and vagueness in many decision-making scenarios often leads to imprecise judgments in conventional AHP approaches [31]. Consequently, several researchers (e.g., [30,32,33]) have focused on studying fuzzy AHP, an extension of Saaty's theory, and have indicated that fuzzy AHP offers a more comprehensive description of the analyzed process than the traditional AHP method (Table 4).…”

Advancing SDGs: Predicting Future Shifts in Saudi Arabia’s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data

“…In recent times, the use of artificial intelligence (AI) and machine learning (ML) has shown potential in enhancing the precision and efficiency of modeling changes in water supplies via the analysis of GRACE satellite data [3,[26][27][28][29]. Different models have been employed, including ANN, fuzzy logic, and hybrid learning [30]. The use of AI-based methods to model GRACE TWS has several advantages.…”

Quantification of Expert Knowledge in Describing COLREGs Linguistic Variables

“…Gaussian membership functions have been used in research to overcome problems with basic triangular and trapezoidal membership functions [46,47]. However, research [48] has found that, on observed data, fuzzy logic system results are more conservative than those of Fuzzy AHP and the equal weights method. This could suggest that the classical fuzzy system is more appropriate for risk assessment.…”