Multicriterion decision making in groundwater planning

Abstract

The groundwater planning problems are often multiobjective. Due to conflicting objectives and non-linearity of the variables involved, several feasible solutions may have to be evolved rather than single optimal solution. In this study, the simulation model built on an Analytic Element Method (AEM) and the optimization model built on a Non-dominated Sorting Genetic Algorithm (NSGA-II) were coupled and applied to study a part of the Dore river catchment, France. The maximization of discharge, the minimization of pumping cost and the minimization of piping cost are the three objectives considered. 2105 non-dominated groundwater planning strategies were generated. K-Means cluster analysis was employed to classify the strategies, and clustering was performed for 3 to 25 clusters. A cluster validation technique, namely Davies–Bouldin (DB) index, was employed to find the optimal number of clusters of groundwater strategies which were found to be 20. Multicriterion Decision-Making (MCDM) techniques, namely VIKOR and TOPSIS, were developed to rank the 20 representative strategies. Both these decision-making techniques preferred representative strategy A5 (piping cost, pumping cost and discharge respectively of 880,000 Euro, 679,000 Euro and 1,263.1 m3/s). The sensitivity analysis of parameter v in VIKOR suggested that there were changes in ranking pattern for various values of v. However, the first position remained unchanged.

Hydroinformatics: A review and future outlook

“…Numerous articles have been published showing the way hydroinformatics approaches have been, and are being, applied to the management of groundwater. Relatively recent ones include those authored by Gaur et al (2021), Parasyris et al (2021), Rao et al (2021), Kumar et al (2022), Pranjal et al (2023), andZamani et al (2023). 6.…”

Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization

“…Evaluating different weight combinations would require extensive analysis which would change the focus of this research. Therefore, here presented multi-objective optimization problem is solved using the Pareto-based optimization algorithm, Nondominated Sorting Genetic Algorithm (NSGA-II; Deb et al 2002), which is widely used for solving multi-objective optimization tasks for water resources (Artina et al 2012;Darvishi & Kordestani 2019;Gao et al 2019;Wang et al 2019;Gaur et al 2021). Here, this algorithm is used to improve the initial tuning solution provided by the manual procedure.…”