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Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

Author

Listed:
  • Long Zhao

    (Henan University of Science and Technology
    Sichuan University)

  • Liwen Xing

    (Sichuan University)

  • Yuhang Wang

    (Henan University of Science and Technology)

  • Ningbo Cui

    (Sichuan University)

  • Hanmi Zhou

    (Henan University of Science and Technology)

  • Yi Shi

    (Henan University of Science and Technology)

  • Sudan Chen

    (Henan University of Science and Technology)

  • Xinbo Zhao

    (Henan University of Science and Technology)

  • Zhe Li

    (Henan University of Science and Technology)

Abstract

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.

Suggested Citation

  • Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:3:d:10.1007_s11269-022-03423-7
    DOI: 10.1007/s11269-022-03423-7
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    References listed on IDEAS

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    1. Milan Gocić & Mohammad Arab Amiri, 2021. "Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1913-1926, April.
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    4. Gonçalo C. Rodrigues & Ricardo P. Braga, 2021. "Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate," Agriculture, MDPI, vol. 11(2), pages 1-13, February.
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