Determining the Optimal Aquifer Exploitation under Artificial Recharge using the Combination of Numerical Models and Particle Swarm Optimization

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Abstract

Determining optimal exploitation from aquifers is always a major challenge, especially for aquifers facing a drop in their groundwater level. In aquifers with artificial recharge, more complex algorithms are required to determine the optimal exploitation amount. Therefore, in this study, the optimal amount of harvest from the exploitation wells has been determined using a combined simulation–optimization model considering the artificial recharge in Yasouj aquifer in Iran. The model is based on a combination of MODFLOW code and gene expression programming (GEP) simulator tool to simulate the aquifer and particle swarm optimization (PSO) to maximize the total exploitation from the aquifer. The simulation results showed that the artificial recharge was ineffective in maximum exploitation from the aquifer. As a result, considering several constraints, including the maximum pumping rate from the aquifer and the permissible drop in the groundwater level, the maximum exploitation from the aquifer was defined as the objective function. The optimization results showed that the optimal exploitation rate is equal to 8.84 million cubic meters (MCM) per year, and only 74% of the water from artificial recharge can be used based on this amount. Additionally, the most appropriate locations to exploit this amount of water are the northwest and east of the aquifer. According to the findings, it is suggested to ban exploitation from the central and southern parts of the aquifer due to the low groundwater level. The results of the sensitivity analysis show that the reduction in the maximum exploitation rate along with a 50% drop in the groundwater level play an effective role in decreasing the optimal exploitation amount.

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APA

Maghsoudi, R., Javadi, S., Shourian, M., & Golmohammadi, G. (2023). Determining the Optimal Aquifer Exploitation under Artificial Recharge using the Combination of Numerical Models and Particle Swarm Optimization. Hydrology, 10(5). https://doi.org/10.3390/hydrology10050100

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