基于径向基神经网络预测日参考作物需水量

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摘要：为了利用有限的气象数据准确预测蓄水坑灌果园的日参考作物需水量，利用蓄水坑灌试验基地逐日温度与湿度数据，构建了基于径向基神经网络的ET0预测模型，并将其模拟结果及Hargreaves、Priestley-Taylor两种常用ET0计算模型的计算结果同FAO-56 Penman-Monteith（FAO56-PM）公式计算的标准值进行对比。结果表明：径向基神经网络预测模型的模拟结果与标准方法FAO56-PM公式的计算结果最接近，而Hargreaves、Priestley-Taylor两个常用计算模型的计算结果比标准值偏大，在实际应用中应对其进行校正。

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关键词： 蓄水坑灌；日参考作物需水量；径向基神经网络；Hargreaves公式；Priestley-Taylor公式

Abstract：This paper aimed to realize the accurate prediction of the daily reference crop water requirement of the water storage pit irrigation apple orchard based on the limited meteorological data. According to the data of daily temperature and humidity data of the water storage pit irrigation orchard at the Institute of Shanxi Academy of Agricultural Sciences, an ET0 prediction model based on radial basis neural network was built. The simulation results and the calculation results of the two commonly used ET0 calculation formulas of Hargreaves and PriestleyTaylor were compared with the standard values calculated by the FAOPM formula. The results show that the simulation results of the radial basis neural network model are closer to the standard values calculated by the FAOPM formula. The calculation results of the Hargreaves formula and the PriestleyTaylor formula are larger than that of the standard values, which should be corrected by coefficients in practical applications.

Key words： water storage pit irrigation; daily reference crop water requirement; radial basis neural network; Hargreaves equation; PriestleyTaylor equation

基金资助：国家自然科学基金资助项目（51579168）

通讯作者介绍：孙西欢（1960—），男，山西太原人，教授，博士生导师，主要从事土壤物理、灌溉排水理论与技术研究工作

作者简介：孟玮（1987—），女，山西太原人，博士研究生，研究方向为节水灌溉理论与技术

引用本文：

孟玮¹，孙西欢^2，3，郭向红²，马娟娟².基于径向基神经网络预测日参考作物需水量[J].人民黄河,2024,46(4):117-120.
MENG Wei¹, SUN Xihuan^2，3, GUO Xianghong², MA Juanjuan².Prediction of Daily Reference Crop Water Requirement Based on Radial Basis Neural Network Model .Yellow River,2024,46(4):117-120.

链接本文:

http://www.rmhh.com.cn/qklb/2024/202404/202404/t20240407_568955.html

2024,Vol. 46(4): 117

节水灌溉

基于径向基神经网络预测日参考作物需水量

孟玮¹，孙西欢^2，3，郭向红²，马娟娟²

（（1.太原理工大学机械与运载工程学院，山西太原 030024; 2.太原理工大学水利科学与工程学院，山西太原 030024;3.晋中学院，山西晋中 030600））

doi:

Prediction of Daily Reference Crop Water Requirement Based on Radial Basis Neural Network Model

MENG Wei¹, SUN Xihuan^2，3, GUO Xianghong², MA Juanjuan²

(1.College of Mechanical and Vehicle Engineering， Taiyuan University of Technology, Taiyuan 030024， China； 2.College of Water Resource Science and Engineering， Taiyuan University of Technology, Taiyuan 030024， China； 3.Jinzhong College, Jinzhong 030600， China)

全文: PDF ()

摘要： 为了利用有限的气象数据准确预测蓄水坑灌果园的日参考作物需水量，利用蓄水坑灌试验基地逐日温度与湿度数据，构建了基于径向基神经网络的ET0预测模型，并将其模拟结果及Hargreaves、Priestley-Taylor两种常用ET0计算模型的计算结果同FAO-56 Penman-Monteith（FAO56-PM）公式计算的标准值进行对比。结果表明：径向基神经网络预测模型的模拟结果与标准方法FAO56-PM公式的计算结果最接近，而Hargreaves、Priestley-Taylor两个常用计算模型的计算结果比标准值偏大，在实际应用中应对其进行校正。

关键词：

Abstract: This paper aimed to realize the accurate prediction of the daily reference crop water requirement of the water storage pit irrigation apple orchard based on the limited meteorological data. According to the data of daily temperature and humidity data of the water storage pit irrigation orchard at the Institute of Shanxi Academy of Agricultural Sciences, an ET0 prediction model based on radial basis neural network was built. The simulation results and the calculation results of the two commonly used ET0 calculation formulas of Hargreaves and PriestleyTaylor were compared with the standard values calculated by the FAOPM formula. The results show that the simulation results of the radial basis neural network model are closer to the standard values calculated by the FAOPM formula. The calculation results of the Hargreaves formula and the PriestleyTaylor formula are larger than that of the standard values, which should be corrected by coefficients in practical applications.

Key words: water storage pit irrigation; daily reference crop water requirement; radial basis neural network; Hargreaves equation; PriestleyTaylor equation

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基金资助: 国家自然科学基金资助项目（51579168）

作者简介: 孟玮（1987—），女，山西太原人，博士研究生，研究方向为节水灌溉理论与技术