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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
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    References listed on IDEAS

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    1. Liu, Haijun & Wang, Xuming & Zhang, Xian & Zhang, Liwei & Li, Yan & Huang, Guanhua, 2017. "Evaluation on the responses of maize (Zea mays L.) growth, yield and water use efficiency to drip irrigation water under mulch condition in the Hetao irrigation District of China," Agricultural Water Management, Elsevier, vol. 179(C), pages 144-157.
    2. Kaihua Liu & Xiyun Jiao & Weihua Guo & Yunhao An & Mohamed Khaled Salahou, 2020. "Improving border irrigation performance with predesigned varied-discharge," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-12, May.
    3. Patel, Neelam & Rajput, T.B.S., 2007. "Effect of drip tape placement depth and irrigation level on yield of potato," Agricultural Water Management, Elsevier, vol. 88(1-3), pages 209-223, March.
    4. Tony Carr & Haishun Yang & Chittaranjan Ray, 2016. "Temporal Variations of Water Productivity in Irrigated Corn: An Analysis of Factors Influencing Yield and Water Use across Central Nebraska," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
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