Developments in agriculture, industry, population growth and climate change lead to increased utilization of groundwater resources, followed by a decrease in groundwater level and an increase in the concentration of pollution in aquifers. Accordingly, the objective of this study was to investigate the impact of optimal groundwater utilization and artificial recharge systems on groundwater level fluctuations and changes in pollution concentration of the Shahriar plain aquifer. Therefore, there was a need for a comprehensive multi-objective simulator-optimizer model that would be able to solve existing problem. For this purpose, a multi-objective modeling platform was developed that included two independent simulator-optimizer models. In the first model, the artificial neural network (ANN) was used to simulate the changes of groundwater level (GWL) and its quality as a function of the TDS index in the Shahriar Plain aquifer. The regression was then used to predict groundwater quality. Finally, the multi-objective genetic algorithm (NSGA-II) was used to optimize groundwater resources harvesting. The second model simulated the flood storage volume in the reservoirs of the artificial recharge system (ARS) using ANN and then optimized utilization of the artificial recharge system using (NSGA-II). The results of the first model showed that the optimal volume of water withdrawn from the aquifer and the optimal TDS value of the aquifer decreased by 29 and 18%, respectively, while the changes of the optimal groundwater level increased by 28 m. Furthermore, based on the second model results, the total volume of optimal recharge will increase by 119% in the studied period due to the artificial recharge system, followed by a 14% increase in the changes of the optimal groundwater level. Overall, it can be concluded that the multi-objective modeling platform method can meet the objectives of this research simultaneously.
Elsevier, Physics and Chemistry of the Earth, Parts A/B/C, Volume 129, 2023, 103358
