A Hybrid Decision Support Model Using a Trapezoidal Fuzzy-Based Multi-Attribute Preference Model with AHP-Entropy for Groundwater Remediation Selection

Determining the most suitable groundwater contamination remediation among multiple discrete alternatives is usually difficult for decision makers and of great significance to ensure the green development of ecological environment. This study proposed a novel multi-attribute preference model (MAPM) method by combining the trapezoidal fuzzy information, analytic hierarchy process (AHP) and entropy method for prioritizing the groundwater contamination remediation among a number of alternatives. This method has the advantages of (1) screening the most desirable remediation without introducing time-consuming nonlinear multi-objective optimization algorithms; (2) addressing uncertain attributes expressed as trapezoidal fuzzy information; (3) assessing priority orders of alternative remediation actions under uncertainty; and (4) being less affected by data inexactness and guaranteeing better environmental performance than MAPM. The method is applied to remediation design of practical naphthalene-contaminated aquifer located in Anhui. A total of 50 alternative remediation strategies with an attributes system which consists of ten attribute in three aspects has been proposed. Results indicate that the developed model is feasible and the priorities of groundwater cleanup systems are greatly different for four remediation schemes. The proposed method can be applied into other study area by modifying the relevant attributes appropriately in order to obtain customized remediation strategies.