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Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure

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  • Seyedzadeh, Amin
  • Maroufpoor, Saman
  • Maroufpoor, Eisa
  • Shiri, Jalal
  • Bozorg-Haddad, Omid
  • Gavazi, Farnoosh

Abstract

One of the effective factors to ensure the desirable operation of drip irrigation systems is the uniform emitter discharge, which is affected by operating pressure and temperature. Accurate estimation of this parameter is crucial for optimal irrigation system management and operation. In this research, the emitter outflow discharge was simulated through artificial intelligence (AI)-based approaches under a wide range of temperature (13−53 °C) and operating pressures (0–240 kPa) variations. The applied AI models included artificial neural networks (ANN), neuro-fuzzy sub-clustering (NF-SC), neuro-fuzzy c-Means clustering (NF-FCM), and least square support vector machine (LS-SVM). The input parameters matrix consisted of operating pressure, water temperature, discharge coefficient, pressure exponent and nominal discharge, while the ratio of measured discharge to nominal discharge (modified coefficient, M) was the output of the models. The applied models were assessed through the robust k-fold testing data scanning mode. The 5-agent Global Performance Indicator (GPI) was used for the final reliable ranking. The results showed that all the applied AI models with an average mean absolute error (MAE) of 8.8% had acceptable accuracy for estimating the modified M coefficient. According to the GPI, the LS-SVM model had the lowest error, followed by the NF-SC model with a slight difference.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:228:y:2020:i:c:s037837741931710x
    DOI: 10.1016/j.agwat.2019.105905
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    References listed on IDEAS

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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. Jamei, Mehdi & Maroufpoor, Saman & Aminpour, Younes & Karbasi, Masoud & Malik, Anurag & Karimi, Bakhtiar, 2022. "Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern," Agricultural Water Management, Elsevier, vol. 270(C).
    3. Ni Gao & Yan Mo & Jiandong Wang & Luhua Yang & Shihong Gong, 2022. "Effects of Flow Path Geometrical Parameters on the Hydraulic Performance of Variable Flow Emitters at the Conventional Water Supply Stage," Agriculture, MDPI, vol. 12(10), pages 1-17, September.
    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. 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).

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