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Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information

Author

Listed:
  • Seydou Traore

    (Texas A&M University)

  • Yufeng Luo

    (Texas A&M University
    Wuhan University)

  • Guy Fipps

    (Texas A&M University)

Abstract

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:15:d:10.1007_s11269-017-1784-5
    DOI: 10.1007/s11269-017-1784-5
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    References listed on IDEAS

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    1. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
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    3. 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.
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    6. Luo, Yufeng & Chang, Xiaomin & Peng, Shizhang & Khan, Shahbaz & Wang, Weiguang & Zheng, Qiang & Cai, Xueliang, 2014. "Short-term forecasting of daily reference evapotranspiration using the Hargreaves–Samani model and temperature forecasts," Agricultural Water Management, Elsevier, vol. 136(C), pages 42-51.
    7. Ozgur Kisi & Taner Cengiz, 2013. "Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3541-3553, August.
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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.
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    Cited by:

    1. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    2. Tabasum Rasool & A. Q. Dar & M. A. Wani, 2021. "Development of a Predictive Equation for Modelling the Infiltration Process Using Gene Expression Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1871-1888, April.
    3. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).

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