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Comparative study of conventional and artificial neural network-based ETo estimation models

机译：基于常规和人工神经网络的ETo估计模型的比较研究

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摘要

Accurate estimation of reference crop evapotranspiration (ETo) is required for several hydrological studies and thus, in the past, a number of ETo estimation methods have been developed with different degree of complexity and data requirement. The present study was carried out to develop artificial neural network (ANN) based reference crop evapotranspiration models corresponding to the ASCE’s best ranking conventional ETo estimation methods (Jensen et al. ASCE Manual and Rep. on Engrg. Pract. no. 70, 1990). Among the radiation methods, FAO-24 radiation (or Rad) method for arid and Turc method for humid region, and among the temperature methods, FAO-24 Blaney–Criddle (or BC) method were studied. The ANN architectures corresponding to the above three less data-intensive methods were developed for four CIMIS (California Irrigation Management Information System) stations, namely, Davis, Castroville, Mulberry, and West Side Field station. The comprehensive ANN architecture developed by Kumar et al. (J Irrig Drain Eng 128(4):224–233, 2002) corresponding to Penman–Monteith (PM) ETo for Davis was also tried for the other three stations. Daily meteorological data for a period of more than 10 years (01 January 1990 to 30 June 2000) were collected from these stations and were used to train, test, and validate the ANN models. Two learning schemes, namely, standard back-propagation with learning rate of 0.2 and standard back-propagation with momentum having learning rate of 0.2 and momentum term of 0.95 were considered. ETo estimation performance of the ANN models was compared with the FAO-56 PM method. It was found that the ANN models gave better closeness to FAO-56 PM ETo than the best ranking method in each category (radiation and temperature). Thus these models can be used for ETo estimation in agreement with climatic data availability, when not all required climatic variables are observed.
机译：一些水文研究需要准确估算参考作物的蒸散量（ETo），因此，在过去，已经开发了许多具有不同复杂程度和数据要求的ETo估算方法。进行本研究以开发基于人工神经网络（ANN）的参考作物蒸散模型，该模型对应于ASCE最佳排名的常规ETo估算方法（Jensen等人，ASCE Manual和Rep。on Engrg。Pract。no。70，1990）。 。在辐射方法中，研究了针对干旱地区的FAO-24辐射（或Rad）方法和对潮湿地区的Turc方法，而在温度方法中，研究了FAO-24 Blaney-Criddle（或BC）方法。针对四个CIMIS（加利福尼亚灌溉管理信息系统）站，即戴维斯，卡斯特罗维尔，桑树和西边野外站，开发了与上述三种数据密集型方法相对应的ANN体系结构。 Kumar等人开发的全面的ANN架构。 （J Irrig Drain Eng 128（4）：224–233，2002）对应于戴维斯的Penman-Monteith（PM）ETo的尝试也用于其他三个站点。从这些站点收集了10年以上（1990年1月1日至2000年6月30日）的每日气象数据，并将其用于训练，测试和验证ANN模型。考虑了两种学习方案，即学习速率为0.2的标准反向传播和学习速率为0.2且动量项为0.95的动量的标准反向传播。将ETO估算的ANN模型的性能与FAO-56 PM方法进行了比较。结果发现，与每个类别（辐射和温度）的最佳排名方法相比，人工神经网络模型与FAO-56 PM ETo的联系更为紧密。因此，当未观察到所有必需的气候变量时，这些模型可用于与气候数据可用性相一致的ETo估算。

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  • 来源
    《Irrigation Science》 |2008年第6期|531-545|共15页
  • 作者

    M. Kumar; A. Bandyopadhyay; N. S. Raghuwanshi; R. Singh;

  • 作者单位

    Agricultural and Food Engineering Department Indian Institute of Technology Kharagpur 721 302 West Bengal India;

    Agricultural and Food Engineering Department Indian Institute of Technology Kharagpur 721 302 West Bengal India;

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