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Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

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  • R. Perea
  • E. Poyato
  • P. Montesinos
  • J. Díaz

Abstract

In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembézar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • R. Perea & E. Poyato & P. Montesinos & J. Díaz, 2015. "Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5551-5567, December.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:15:p:5551-5567
    DOI: 10.1007/s11269-015-1134-4
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    1. Van Aelst, P. & Ragab, R. A. & Feyen, J. & Raes, D., 1988. "Improving irrigation management by modelling the irrigation schedule," Agricultural Water Management, Elsevier, vol. 13(2-4), pages 113-125, June.
    2. Playan, Enrique & Mateos, Luciano, 2006. "Modernization and optimization of irrigation systems to increase water productivity," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 100-116, February.
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    1. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
    2. Forouhar, Leila & Wu, Wenyan & Wang, Q.J. & Hakala, Kirsti, 2022. "A hybrid framework for short-term irrigation demand forecasting," Agricultural Water Management, Elsevier, vol. 273(C).
    3. Crespo Chacón, Miguel & Rodríguez Díaz, Juan Antonio & García Morillo, Jorge & McNabola, Aonghus, 2020. "Estimating regional potential for micro-hydropower energy recovery in irrigation networks on a large geographical scale," Renewable Energy, Elsevier, vol. 155(C), pages 396-406.
    4. Playán, Enrique & Salvador, Raquel & Bonet, Luis & Camacho, Emilio & Intrigliolo, Diego S. & Moreno, Miguel A. & Rodríguez-Díaz, Juan A. & Tarjuelo, José M. & Madurga, Cristina & Zazo, Teresa & Sánche, 2018. "Assessing telemetry and remote control systems for water users associations in Spain," Agricultural Water Management, Elsevier, vol. 202(C), pages 89-98.
    5. I. Fernández García & P. Montesinos & E. Camacho Poyato & J. A. Rodríguez Díaz, 2017. "Optimal Design of Pressurized Irrigation Networks to Minimize the Operational Cost under Different Management Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1995-2010, April.
    6. Armario Benitez, Julia I., 2020. "Land, water and energy: the crossing of governance," UC3M Working papers. Economics 31463, Universidad Carlos III de Madrid. Departamento de Economía.
    7. González Perea, R. & Camacho Poyato, E. & Rodríguez Díaz, J.A., 2021. "Forecasting of applied irrigation depths at farm level for energy tariff periods using Coactive neuro-genetic fuzzy system," Agricultural Water Management, Elsevier, vol. 256(C).
    8. Md Mahmudul Haque & Amaury Souza & Ataur Rahman, 2017. "Water Demand Modelling Using Independent Component Regression Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 299-312, January.
    9. I. Tsakmakis & N. Kokkos & V. Pisinaras & V. Papaevangelou & E. Hatzigiannakis & G. Arampatzis & G.D. Gikas & R. Linker & S. Zoras & V. Evagelopoulos & V.A. Tsihrintzis & A. Battilani & G. Sylaios, 2017. "Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 563-580, January.

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