Simulation-Optimization based Inverse Groundwater Modeling to Estimate Aquifer Parameters

Hydrogeological parameter such as transmissivity plays a vital role in assessing the groundwater potential, estimating the impact of pumping, taking remediation and preventive measures on an aquifer. It is necessary to estimate the flow parameters accurately. Experimental techniques available to find out the aquifer parameters are expensive and time-consuming. Hence, mathematical modeling techniques were used extensively to estimate the aquifer parameters. In this study, a forward simulation model for groundwater flow is developed with meshless local radial point interpolation method and is coupled with two different optimization techniques (such as particle swarm optimization (PSO), grey wolf optimization (GWO)) to form the simulation–optimization (SO) model. The objective of the developed SO model is to minimize the error between the true value and predicted value of the unknown system parameter of transmissivity using the known state variable of observed groundwater head and control variables (such as pumping, recharge). The developed SO model is applied to estimate the aquifer parameters in a hypothetical problem. It is observed that PSO approach gives accurate estimation of aquifer parameters than GWO for the problem considered.