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Decision Support System Based on Genetic Algorithms to Optimize the Daily Management of Water Abstraction from Multiple Groundwater Supply Sources

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  • Rafael Gonzalez Perea

    (University of Castilla-La Mancha)

  • Miguel Ángel Moreno

    (University of Castilla-La Mancha)

  • Victor Buono Silva Baptista

    (University of Lavras)

  • Juan Ignacio Córcoles

    (Section of Solar and Energy Efficiency)

Abstract

The use of irrigation water extracted from aquifers with submerged pumps is essential to ensure agricultural production mainly in water-scarce regions.. However, the use of the water source requires of a considerable energy consumption by water user associations (WUAs) being key factor to consider due to their high share of total management, operation, and maintenance costs. In this work, a new tool (MOPWE, model to optimize water extraction) to optimize the water and energy use of wells in WUAs was developed. MOPWE was applied to a real WUA located in Castilla-La Mancha region (southeast of Spain). This WUA utilizes groundwater as water source that is extracted from several different wells of different characteristics (discharges, water table levels, efficiency, variable speed drives…).. Therefore, these kind of WUAs must decide not only which well to activate at a certain time but also at what frequency the variable-speed drive should run the pump. With the aim of aiding decision-making in groundwater abstraction, a new management model (MOPWE), which is based on multi-objective genetic algorithms and is implemented in MATLAB®. This model helps determine the optimal daily management of a WUA with multiple underground supply sources and focuses on the management of wells while considering the water reservoir level. After 18,000 generations of the genetic algorithm, the pareto front was obtained with the best WUA managements achieving a water and energy savings of 25% and 54%, respectively. At the end of the irrigation season, the optimal total energy consumption per unit of water applied was 38% lower than that achieved by the current management. Results showed that a more realistic approach can be implemented when several water supplies operate jointly under a collaborative principle.

Suggested Citation

  • Rafael Gonzalez Perea & Miguel Ángel Moreno & Victor Buono Silva Baptista & Juan Ignacio Córcoles, 2020. "Decision Support System Based on Genetic Algorithms to Optimize the Daily Management of Water Abstraction from Multiple Groundwater Supply Sources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4739-4755, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02687-1
    DOI: 10.1007/s11269-020-02687-1
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    References listed on IDEAS

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    1. Lima, F.A & Martínez-Romero, A. & Tarjuelo, J.M. & Córcoles, J.I., 2018. "Model for management of an on-demand irrigation network based on irrigation scheduling of crops to minimize energy use (Part I): Model Development," Agricultural Water Management, Elsevier, vol. 210(C), pages 49-58.
    2. R. Khadra & M. A Moreno & H. Awada & N. Lamaddalena, 2016. "Energy and Hydraulic Performance-Based Management of Large-Scale Pressurized Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3493-3506, August.
    3. R. González Perea & E. Camacho Poyato & P. Montesinos & J. A. Rodríguez Díaz, 2016. "Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2815-2830, June.
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    Cited by:

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    2. Rashid, Muhammad Usman & Abid, Irfan & Latif, Abid, 2022. "Optimization of hydropower and related benefits through Cascade Reservoirs for sustainable economic growth," Renewable Energy, Elsevier, vol. 185(C), pages 241-254.
    3. Jiqing Li & Jing Huang & Pengteng Liang & Jay R. Lund, 2021. "Fuzzy Representation of Environmental Flow in Multi-Objective Risk Analysis of Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2845-2861, July.
    4. M. Mora & H. Puerto & C. Rocamora & R. Abadia, 2021. "New Indicators to Discriminate the Cause of Low Energy Efficiency in Deep-Well Pumps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1373-1388, March.
    5. Radhikesh Kumar & Maheshwari Prasad Singh & Bishwajit Roy & Afzal Hussain Shahid, 2021. "A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1927-1960, April.

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