Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models

Abstract

Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very highvulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.

Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods

“…In a study conducted in Kerala by Karunakaran et al, ( 2014 ), altered sensorium, hypertension, and patients over the age of 40 (who were 9.3 times more likely to die) were revealed to be independent predictors of dengue death. This underlines the need for vulnerability modelling by integrating socio-economic and physico-environmental indicators (as in Senan et al, 2022 ).…”

“…This fact is confirmed by the prediction accuracy score (AUC value) of the maps created in this study. The studies by Akshaya et al, ( 2021 ), Meshram et al, ( 2019 ), Tripathi et al, ( 2021 ), and Senan et al, ( 2022 ) also found F-AHP as more effective than the AHP model. Validation of the created maps is the integral part of any modelling (susceptibility, risk, or vulnerability) due to the fact that "unless validated, the map is of no operational use".…”

“…This method works to deconstruct complex problems into a hierarchy and identify the solution most suited to the objective (Qazi & Abushammala, 2020 ). The generation of the matrix for pair-wise comparisons, calculation of the eigen vector, weighting coefficient (Table 2 ), and consistency ratio (Table 3 ) are the essential procedures (Akshaya et al, 2021 ; Senan et al, 2022 ).where Elev. is the elevation, HD is the household density, and PD is the population density.…”

“…Buckley ( 1985 ) proposed a method for comparing fuzzy ratios defined as triangle membership functions, which was used in this research. Pair-wise comparison matrix creation (see Table 4 ), geometric mean calculation (see Table 5 ), computation of relative fuzzy weights (see Table 6 ) and calculation of averaged and normalised relative weight are the essential procedures involved (see Table 7 ) (Akshaya et al, 2021 ; Senan et al, 2022 ). The steps in F-AHP modelling are as follows:…”

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

“…However, so far, the AHP and F-AHP models have never been compared for wildfire risk modeling. The AHP and F-AHP models have been compared only for assessing the efficacy of landslide susceptibility [53,54], flood susceptibility [55,56], flood vulnerability [57], and forest fire susceptibility [52]. This is the uniqueness of this study, as no researchers have assessed the prediction capability of both the AHP and F-AHP models for the demarcation of wildfire risk zones and applied different models for the comparison of two protected areas with different vegetation in any part of the world.…”