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Forecasting of applied irrigation depths at farm level for energy tariff periods using Coactive neuro-genetic fuzzy system

Author

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  • González Perea, R.
  • Camacho Poyato, E.
  • Rodríguez Díaz, J.A.

Abstract

Nowadays, water scarcity and the increase in energy demand and their associated costs in pressurized irrigation systems are causing serious challenges. In addition, most of these pressurized irrigation systems has been designed to be operated on-demand where irrigation water is continuously available to farmers complexing the daily decision-making process of the water user association’ managers. Know in advance how much water will be applied by each farmer and its distribution during the day would facilitate the management of the system and would help to optimize the water use and energy costs. In this work, a new hybrid methodology (CANGENFIS) combining Multiple input -Multiple output, fuzzy logic, artificial neural networks and multiobjective genetic algorithms was developed to model farmer behaviour and short-term forecasting the distribution by tariff period of the irrigation depth applied at farm level. CANGENFIS which was developed in Matlab was applied to a real water user association located in Southwest Spain. Three optimal models for the main crops in the water user association were obtained. The average for all tariff periods of the representability (R2) and accuracy of the forecasts (standard error prediction, SEP) were 0.70, 0.76% and 0.85% and 19.9%, 22.9% and 19.5%, for rice, maize and tomato crops models, respectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003334
    DOI: 10.1016/j.agwat.2021.107068
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    References listed on IDEAS

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    1. 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.
    2. Bazilian, Morgan & Rogner, Holger & Howells, Mark & Hermann, Sebastian & Arent, Douglas & Gielen, Dolf & Steduto, Pasquale & Mueller, Alexander & Komor, Paul & Tol, Richard S.J. & Yumkella, Kandeh K., 2011. "Considering the energy, water and food nexus: Towards an integrated modelling approach," Energy Policy, Elsevier, vol. 39(12), pages 7896-7906.
    3. Farshad Rezaei & Hamid R. Safavi & Maryam Zekri, 2017. "A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1139-1155, March.
    4. González Perea, R. & Camacho Poyato, E. & Montesinos, P. & Rodríguez Díaz, J.A., 2018. "Prediction of applied irrigation depths at farm level using artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 206(C), pages 229-240.
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