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Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables

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  • Maroufpoor, Saman
  • Shiri, Jalal
  • Maroufpoor, Eisa

Abstract

The coefficient of uniformity (CU), an important parameter in design of irrigation systems, affects the quality and return of investment in irrigation projects significantly, and is a good indicator of water losses. In this paper, a single model was proposed to obtain the CU values in four sprinkler types of ZK30, ZM22, AMBO, and LUXOR. Average wind speed, coarseness index (large and small nozzle diameters), and sprinkler/lateral spacing were used as input parameters to obtain the CU values through employing the artificial neural networks (ANN), neuro-fuzzy grid partitioning (NF-GP), neuro-fuzzy sub-clustering (NF-SC), least square support vector machine (LS-SVM) and gene expression programming (GEP) techniques. The available data set consisted of 294 samples that were used to evaluate the proposed methodology. The applied techniques were assessed through the robust k-fold testing data assignment mode. Based on the results, all the applied models presented good capability in estimating CU. The obtained results revealed that the coarseness index (large nozzle diameter) had the lowest impact on modeling CU is sprinkler irrigation systems.

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  • Maroufpoor, Saman & Shiri, Jalal & Maroufpoor, Eisa, 2019. "Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables," Agricultural Water Management, Elsevier, vol. 215(C), pages 63-73.
    • Handle: RePEc:eee:agiwat:v:215:y:2019:i:c:p:63-73
    DOI: 10.1016/j.agwat.2019.01.008
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    Cited by:

    1. Samad Emamgholizadeh & Amin Seyedzadeh & Hadi Sanikhani & Eisa Maroufpoor & Gholamhosein Karami, 2022. "Numerical and artificial intelligence models for predicting the water advance in border irrigation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 558-575, January.
    2. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
    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. Kisi, Ozgur & Khosravinia, Payam & Heddam, Salim & Karimi, Bakhtiar & Karimi, Nazir, 2021. "Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algor," Agricultural Water Management, Elsevier, vol. 256(C).
    5. Ali Barzkar & Mohammad Najafzadeh & Farshad Homaei, 2022. "Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1931-1952, February.
    6. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).
    7. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).

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