Inverse modeling application for aquifer parameters estimation using a precise simulation-optimization model

In this research, a simulation-optimization (S/O) model is used in order to estimate aquifer parameters on two aquifers. In this model, meshless local Petrov-Galerkin (MLPG) is used for simulation purpose and modified teaching-learning-based optimization (MTLBO) algorithm is engaged as optimization model. Linking these two powerful models generates a S/O model named MLPG-MTLBO. The proposed model is applied on two aquifers: a standard and a real field aquifer. In standard aquifer, parameters are only transmissivity coefficients in x and y direction for three zones. The acquired results by MLPG-MTLBO are really close to true values. This fact presents the power of MLPG-MTLBO inverse model. Therefore, it is applied on field aquifer. Unconfined aquifer of Birjand recognized as real case study. Parameters which are needed to be estimated are specific yields and hydraulic conductivity coefficients. These parameters are computed by MLPG-MTLBO and entered to the groundwater flow model. The achieved groundwater table compared with observation data and RMSE is calculated. RMSE value is 0.356 m; however, this error criterion for MLPG and FDM is 0.757 m and 1.197 m, respectively. This means that estimation is precise and makes the RMSE to reduce from 0.757 to 0.356 m, and also, MLPG-MTLBO is an accurate model for this aim.

Automated summary

In this research, a simulation-optimization model called MLPG-MTLBO is used to estimate aquifer parameters on two aquifers. The model combines the meshless local Petrov-Galerkin simulation with the modified teaching-learning-based optimization algorithm. The results show that the MLPG-MTLBO model is accurate in estimating the aquifer parameters and can be applied to real field aquifers.