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Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables

Author

Listed:
  • Mohammad Taghi Sattari

    (Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 51666, Iran
    Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey)

  • Halit Apaydin

    (Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam)

Abstract

The amount of water allocated to irrigation systems is significantly greater than the amount allocated to other sectors. Thus, irrigation water demand management is at the center of the attention of the Ministry of Agriculture and Forestry in Turkey. To plan more effective irrigation systems in agriculture, it is necessary to accurately calculate plant water requirements. In this study, daily reference evapotranspiration (ETo) values were estimated using tree-based regression and deep learning-based gated recurrent unit (GRU) models. For this purpose, 15 input scenarios, consisting of meteorological variables including maximum and minimum temperature, wind speed, maximum and minimum relative humidity, dew point temperature, and sunshine duration, were considered. ETo values calculated according to the United Nations Food and Agriculture Organization (FAO) Penman-Monteith method were considered as model outputs. The results indicate that the random forest model, with a correlation coefficient of 0.9926, is better than the other tree-based models. In addition, the GRU model, with R = 0.9837, presents good performance relative to the other models. In this study, it was found that maximum temperature was more effective in estimating ETo than other variables.

Suggested Citation

  • Mohammad Taghi Sattari & Halit Apaydin & Shahaboddin Shamshirband, 2020. "Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:972-:d:371047
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    References listed on IDEAS

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    1. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
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    More about this item

    Keywords

    reference evapotranspiration; deep learning; M5 tree model; random forest; random tree; regression tree;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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