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Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater

Author

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  • Yousefi, Maryam
  • Banihabib, Mohammad Ebrahim
  • Soltani, Jaber
  • Roozbahani, Abbas

Abstract

In this research, cropping pattern optimization models have been developed to maximize the benefits and minimize the potential negative effects of quantitative-qualitative conjunctive use of unconventional surface water (treated wastewater) and groundwater in agricultural irrigation. Three objective functions were considered: maximizing the benefits from crop patterns, reducing nitrogen leaching, and improving the rate of aquifer recharge. The developed models use particle swarm optimization (PSO) integrated with an additive weighting method and a multi-objective particle swarm optimization (MOPSO) algorithm for different single- and three-objective optimization scenarios in the Varamin irrigation network in Iran, and then the results of the models were compared. The solutions resulting from the three-objective model using the PSO algorithm with the additive weighting method indicated that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 49%, and 20%, respectively. Meanwhile, the conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 35% and 88%, respectively. The solutions resulting from the three-objective model using the MOPSO algorithm, forming Pareto front, and then using TOPSIS to select the optimal solution from among the non-dominated solutions showed that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 47% and 15%, respectively. The objectives of conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 36% and 89%, respectively. The difference between the objective function values of the two algorithms is about 0.4% to 4%, which shows the proximity of the models in finding the optimal solution. The results of this research can be used to optimize the use of water resources, increase farmers’ benefits, and decrease nitrogen leaching from irrigation and drainage networks.

Suggested Citation

  • Yousefi, Maryam & Banihabib, Mohammad Ebrahim & Soltani, Jaber & Roozbahani, Abbas, 2018. "Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater," Agricultural Water Management, Elsevier, vol. 208(C), pages 224-231.
    • Handle: RePEc:eee:agiwat:v:208:y:2018:i:c:p:224-231
    DOI: 10.1016/j.agwat.2018.06.025
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    References listed on IDEAS

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    5. Reza Sepahvand & Hamid R. Safavi & Farshad Rezaei, 2019. "Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2123-2137, April.
    6. Zahra Kayhomayoon & Sami Ghordoyee Milan & Naser Arya Azar & Pete Bettinger & Faezeh Babaian & Abolfazl Jaafari, 2022. "A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    7. Jain, Sonal & Ramesh, Dharavath & Trivedi, Munesh C. & Edla, Damodar Reddy, 2023. "Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production," Agricultural Water Management, Elsevier, vol. 279(C).
    8. Mehrabi, Ahmad & Heidarpour, Manouchehr & Safavi, Hamid R. & Rezaei, Farshad, 2021. "Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm," Agricultural Water Management, Elsevier, vol. 246(C).
    9. Gong, Xinghui & Zhang, Hongbo & Ren, Chongfeng & Sun, Dongyong & Yang, Jiantao, 2020. "Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty," Agricultural Water Management, Elsevier, vol. 239(C).

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