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Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production

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  • Jain, Sonal
  • Ramesh, Dharavath
  • Trivedi, Munesh C.
  • Edla, Damodar Reddy

Abstract

Extensive variability in current climatic conditions necessitates the need for optimal planning of water resources to manage socio-economic and environmental requirements efficiently. The optimal allocation of surface water and groundwater is essential to maximize crop net return due to uncertainty in seasonal rainfall, groundwater, and surface water availability in each region. This study demonstrated a multi-objective model to maximize the crop net return and efficient management of water resources. The multi-objective model comprised three objective functions maximizing the crop net return, minimizing the water deficit, and maximizing the aquifer recharge. The model’s practicability was analyzed through the case study of the Pennar-Palar-Cauvery link canal command in India. Three meta-heuristic approaches, particle swarm optimization (PSO), genetic algorithm (GA), and marine predators algorithm (MPA), were employed to solve the presented model. And their performance was evaluated using hypervolume and coverage metrics, indicating MPA’s superiority in obtaining well-distributed Pareto-optimal solutions. Thus, decision-makers can choose the best feasible solution based on current resource availability and preferences. The model was executed considering different cropping area deviations (5–25%) in the existing cropping pattern and allowance of groundwater mining from 0% to 70%. The obtained cropping pattern utilizing MPA at 25% cropping deviation achieved an increased net return of 1327.05 million INR.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:279:y:2023:i:c:s037837742300046x
    DOI: 10.1016/j.agwat.2023.108181
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    References listed on IDEAS

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    1. Rapeepan Pitakaso & Kanchana Sethanan & Kim Hua Tan & Ajay Kumar, 2024. "A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem," Annals of Operations Research, Springer, vol. 342(1), pages 621-656, November.
    2. Saeid Akbarifard & Mohamad Reza Madadi & Mohammad Zounemat-Kermani, 2024. "An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Mardani Najafabadi, Mostafa & Magazzino, Cosimo & Valente, Donatella & Mirzaei, Abbas & Petrosillo, Irene, 2023. "A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus," Ecological Modelling, Elsevier, vol. 484(C).

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