Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Abstract

International audienceGroundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle swam optimization (PSO) is adopted. The analytic element method (AEM) is employed to get at the operating potentiometric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out varying the parameters and it is shown that the effect of any such variation is quite significant

Vectorized simulation of groundwater flow and contaminant transport using analytic element method and random walk particle tracking

“…The superposition of analytic solutions is valid for any linearized groundwater flow equation. In the AEM, groundwater flow is generally expressed with reference to complex potential (Ω) (Gaur et al, ): where Φ is the discharge potential (L 3 T −1 ) and ψ is the stream function (L 3 T −1 ). In a combined confined and unconfined aquifer, the discharge potential Φ can be expressed as (Batista et al, ; Strack, ) where H is the thickness of aquifer (L) and b is the elevation of aquifer base (L).…”

“…The superposition of analytic solutions is valid for any linearized groundwater flow equation. In the AEM, groundwater flow is generally expressed with reference to complex potential (Ω) (Gaur et al, 2015):…”

Monte Carlo-based interval transformation analysis for multi-criteria decision analysis of groundwater management strategies under uncertain naphthalene concentrations and health risks

“…Because of the inherent natural heterogeneity and lack of complete knowledge of the physical, chemical, and biological processes, identification of a suitable groundwater management technology is a complex process and uncertainty in groundwater management problems often stems from uncertain geological or geoenvironmental parameters (e.g. intrinsic permeability, soil porosity, migration velocity, NAPL saturation and so on) (He et al, 2008;Fan et al, 2014;Gaur et al, 2015;Srivastava and Singh, 2015). In real-world groundwater management, many inexactness and imprecision parameters maybe discrete and irregularly distributed, thus traditional inexact MCDA approaches would fail to work (Warner et al, 2006;Yan and Minsker, 2011;Compernolle et al, 2014;Chitsazan and Tsai, 2015).…”

“…Luo et al (2014) developed a new probabilistic multi-objective fast harmony search algorithm for optimal design of groundwater management systems under uncertainty. Gaur et al (2015) developed a multi-objective fuzzy optimization model considering three conflicting objectives by using particle Swarm optimization and analytic element method for obtaining a sustainable groundwater management policy. For real-world groundwater management problems, decision makers often need to simultaneously consider some competing criteria such as cleanup time, management cost, health risks and contaminant mass remaining in the aquifers with a variety of uncertainties.…”