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Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling

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  • Feng, Yu
  • Cui, Ningbo
  • Gong, Daozhi
  • Zhang, Qingwen
  • Zhao, Lu

Abstract

Accurate estimation of reference evapotranspiration (ET0) is of importance for regional water resource management. The present study proposed two artificial intelligence models, random forests (RF) and generalized regression neural networks (GRNN), for daily ET0 estimation. Meteorological data including maximum/minimum air temperature, solar radiation, relative humidity, and wind speed during 2009∼2014 from two stations in southwest China were used to train and test the RF and GRNN models by using two input combinations, including complete data and only temperature and extraterrestrial radiation (Ra) data. The k-fold test was adopted to test the performance of models according to temporal and spatial criteria and data set scanning procedures. The results indicated that both local and external RF and GRNN models performed well for estimating daily ET0, and RF was slightly better than GRNN generally. The high fluctuations in the accuracy ranges justify the importance of applying k-fold test for assessing the model performance, which could avoid drawing partially valid conclusions from model assessments based on simple data set assignment. Overall, both temperature-based RF and GRNN models can accurately estimate daily ET0, which is helpful for irrigation scheduling in southwest China.

Suggested Citation

  • Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    • Handle: RePEc:eee:agiwat:v:193:y:2017:i:c:p:163-173
    DOI: 10.1016/j.agwat.2017.08.003
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    References listed on IDEAS

    as
    1. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
    2. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    3. Martí, Pau & González-Altozano, Pablo & López-Urrea, Ramón & Mancha, Luis A. & Shiri, Jalal, 2015. "Modeling reference evapotranspiration with calculated targets. Assessment and implications," Agricultural Water Management, Elsevier, vol. 149(C), pages 81-90.
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    5. Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
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