IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i2d10.1007_s10668-022-02878-3.html
   My bibliography  Save this article

Evaluating performance indicators of irrigation systems using swarm intelligence methods in Lake Urmia basin, Iran

Author

Listed:
  • Hossein Dehghanisanij

    (Agricultural Engineering Research Institute)

  • Somayeh Emami

    (University of Tabriz)

  • Hojjat Emami

    (University of Bonab)

  • Ahmed Elbeltagi

    (Mansoura University)

Abstract

The main challenge in the agricultural sector is to increase crop production with minimal water consumption. In this regard, various high efficient irrigation systems have received particular attention. This paper investigates drip (tape) irrigation (DTI). It estimates water productivity (WP), application efficiency (AE) and corn yield in the Miandoab region located in the southeast of Lake Urmia using swarm intelligence methods. The studies were performed in-field monitoring by using a hybrid approach based on an extreme machine learning method (ELM) and tree growth optimization algorithm (TGO) to estimate corn yield, WP, and AE criteria. TGO-ELM method was evaluated on a dataset including irrigation-fertilizer, climate, and soil characteristics of corn fields from 2020 to 2021. The DTI method was compared with the standard furrow irrigation (FI). The field monitoring results showed that the DTI increased corn yield, WP, and AE by 22.2%, 0.58 kg/m3, and 70%, respectively, compared to the FI method. The modeling results showed that model M9 gave a more optimistic estimate of the corn crop, WP and AE by applying the parameters of irrigation levels, rainfall, and soil moisture as model inputs. Also, TGO-ELM with optimal values ​​of R2 = 0.988, RMSE = 0.005, NSE = 0.981, and MAPE = 0.812 had a good performance compared to similar intelligent methods. The results confirmed that TGO-ELM can provide a more accurate estimation of corn yield compared to other similar methods.

Suggested Citation

  • Hossein Dehghanisanij & Somayeh Emami & Hojjat Emami & Ahmed Elbeltagi, 2024. "Evaluating performance indicators of irrigation systems using swarm intelligence methods in Lake Urmia basin, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(2), pages 4175-4195, February.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:2:d:10.1007_s10668-022-02878-3
    DOI: 10.1007/s10668-022-02878-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02878-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02878-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:26:y:2024:i:2:d:10.1007_s10668-022-02878-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.