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Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts

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  • Yufeng Luo
  • Seydou Traore
  • Xinwei Lyu
  • Weiguang Wang
  • Ying Wang
  • Yongyu Xie
  • Xiyun Jiao
  • Guy Fipps

Abstract

Medium range daily reference evapotranspiration (ET o ) forecasts are very helpful for farmers or irrigation system operators for improving their irrigation scheduling. We tested four artificial neural networks (ANNs) for ET o forecasting using forecasted temperatures data retrieved from public weather forecasts. Daily meteorological data were collected to train and validate the ANNs against the Penman–Monteith (PM) model. And then, the temperature forecasts for 7-day ahead were entered into the validated ANNs to produce ET o forecast outputs. The forecasting performances of models were evaluated through comparisons between the ET o forecasted by ANNs and ET o calculated by PM from the observed meteorological data. The correlation coefficients between observed and forecasted temperatures for all stations were all greater than 0.91, and the accuracy of the minimum temperature forecast (error within ± 2 °C) ranged from 68.34 to 91.61 %, whereas for the maximum temperature it ranged from 51.78 to 57.44 %. The accuracy of the ET o forecast (error within ± 1.5 mm day −1 ) ranged from 75.53 to 78.14 %, the average values of the mean absolute error ranged from 0.99 to 1.09 mm day −1 , the average values of the root mean square error ranged from 0.87 to 1.36 mm day −1 , and the average values of the correlation coefficient ranged from 0.70 to 0.75. The results suggested that ANNs can be considered as a promising ET o forecasting tool. The forecasting performance can be improved by promoting temperature forecast accuracy. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:10:p:3863-3876
    DOI: 10.1007/s11269-015-1033-8
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    4. Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
    5. Dang, Chiheng & Zhang, Hongbo & Yao, Congcong & Mu, Dengrui & Lyu, Fengguang & Zhang, Yu & Zhang, Shuqi, 2024. "IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change," Agricultural Water Management, Elsevier, vol. 291(C).
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    9. Yang, Yang & Cui, Yuanlai & Luo, Yufeng & Lyu, Xinwei & Traore, Seydou & Khan, Shahbaz & Wang, Weiguang, 2016. "Short-term forecasting of daily reference evapotranspiration using the Penman-Monteith model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 177(C), pages 329-339.
    10. Han Chen & Jinhui Jeanne Huang & Kai Wang & Edward McBean, 2020. "Quantitative Assessment of Agricultural Practices on Farmland Evapotranspiration Using EddyCovariance Method and Numerical Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 515-527, January.
    11. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    12. 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.
    13. Jia Luo & Xianming Dou & Mingguo Ma, 2022. "Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    14. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Junzeng Xu & Junmei Wang & Qi Wei & Yanhua Wang, 2016. "Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2055-2073, April.

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