Simulation–optimization Models for Aquifer Parameter Estimation

Abstract

An accurate estimation of aquifer parameters is important for effective groundwater management and future scenario prediction. These parameters are mostly obtained through different time-consuming and cumbersome field pum** tests. The inverse problem is a recently developed widely accepted mathematical approach to obtain the representative optimal aquifer parameters, particularly in large heterogeneous aquifer systems. For the inverse problem solution, the simulation–optimization (SO) model approach has been effectively used. The efficiency of these SO models depends mainly on two factors like, the accuracy of the simulation model and the ability of the optimization algorithm to explore the solution space. In this study, we selected the combination of two simulation models (i.e., FEM and Meshfree method) and four optimization algorithms (i.e., Particle Swarm Optimization (PSO), Differential Evolution (DE), a hybrid version of DE and PSO (DE-PSO) and Co-variance Matrix Adaptation Evolution Strategy (CMA-ES)) which resulted into the development of total eight number of SO models. These models are successfully applied to a synthetic confined aquifer problem. The obtained results showed the better performance of the Mfree-CMA-ES compared to its other counterparts like: FEM-DE, Mfree-DE, FEM-PSO, Mfree-PSO, FEM-CMA-ES and Mfree-DE-PSO in terms of convergence and a higher degree of unanimity with the known values of transmissivity and hydraulic conductivity.