Probabilistic Assessment of Flood Susceptibility via a Coparticipative Multicriteria Decision Analysis
Journal
Environmental Processes
Date Issued
April 14, 2025
Abstract
This study aims to develop a coparticipative, multicriteria decision analysis within a probabilistic framework to assess flood susceptibility and quantify the uncertainties associated with the determination of weight coefficients. Seven factors were selected on the basis of discussions with key stakeholders and the literature, namely, the terrain slope, terrain elevation, flow accumulation, distance from the drainage network, soil, rainfall intensity, and land use. The opinions of twelve flood experts are collected and used in decision-making analysis via an analytical hierarchical process (AHP). The outputs of these comparisons are used to conduct Monte Carlo (MC) simulations separately for each pairwise element (MC-AHP), resulting in 151 consistent samples of weight coefficients that are used to calculate the cumulative distribution function of susceptibility scores in the Yermasoyia watershed (Cyprus). The comparison between AHP and MC-AHP predictions revealed that the standard deviations of the criteria are significantly lower in the latter case (0.01–0.04) than in the original dataset (0.03–0.13), whereas a reduction in the skewness values is observed for five out of seven criteria. The MC-AHP results indicate the presence of low-to-intermediate uncertainty levels in the southern regions, whereas moderate-to-high values are predicted in the northern region. In addition, MC-AHP correctly classified known flood-prone locations in the southern part of the study area as having either “Very High” (> 70%) or “High” (> 24%) probability of flood occurrence. The entire methodology is applicable to other scientific disciplines and more advanced MCDA methods, contributing to the inclusion of probabilistic multihazard assessments in decision-making.
Funding
This work was supported by the AGREEMAR project, which is part of the PRIMA programme that is supported under Horizon 2020 by the European Union’s Framework for Research and Innovation (P2P/PRIMA/0321/0024). Financial support is also provided by the “EXCELSIOR” project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No 857510).