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Prediction of applied irrigation depths at farm level using artificial intelligence techniques

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

Abstract

Irrigation water demand is highly variable and depends on farmer behaviour, which affects the performance of irrigation networks. The irrigation depth applied to each farm also depends on farmer behaviour and is affected by precise and imprecise variables. In this work, a hybrid methodology combining artificial neural networks, fuzzy logic and genetic algorithms was developed to model farmer behaviour and forecast the daily irrigation depth used by each farmer. The models were tested in a real irrigation district located in southwest Spain. Three optimal models for the main crops in the irrigation district were obtained. The representability (R2) and accuracy of the predictions (standard error prediction, SEP) were 0.72, 0.87 and 0.72; and 22.20%, 9.80% and 23.42%, for rice, maize and tomato crop models, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:206:y:2018:i:c:p:229-240
    DOI: 10.1016/j.agwat.2018.05.019
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    References listed on IDEAS

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    Cited by:

    1. Zhongwei Liang & Tao Zou & Yupeng Zhang & Jinrui Xiao & Xiaochu Liu, 2022. "Sprinkler Drip Infiltration Quality Prediction for Moisture Space Distribution Using RSAE-NPSO," Agriculture, MDPI, vol. 12(5), pages 1-32, May.
    2. 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).
    3. Seyedzadeh, Amin & Maroufpoor, Saman & Maroufpoor, Eisa & Shiri, Jalal & Bozorg-Haddad, Omid & Gavazi, Farnoosh, 2020. "Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure," Agricultural Water Management, Elsevier, vol. 228(C).
    4. Pazouki, Ehsan, 2021. "A practical surface irrigation design based on fuzzy logic and meta-heuristic algorithms," Agricultural Water Management, Elsevier, vol. 256(C).

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