Generalization of ETo ANN Models through Data Supplanting
Generalization of ETo ANN Models through Data Supplanting
复制标题
通过数据替换来推广 ETo ANN 模型
DOI：
10.1061/(asce)ir.1943-4774.0000152
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
G. Palau
中科院分区：
文献类型：
--
作者：
P. Martí;A. Royuela;J. Manzano;G. Palau
本文介绍了应用人工神经网络（ANN）估计参考蒸散量（ETo）作为当地最高和最低气温的函数，以及外源相对湿度和参考蒸散量在不同的大陆背景下的自治瓦伦西亚地区，在西班牙地中海沿岸。需要根据最低限度的气候数据开发新的和更精确的预测ETO的模型，因为提供可接受结果的现有方法的应用仅限于那些可获得大量可靠气候数据的地方。粮农组织提出的用于ETO预测的Penman-Monteith模型是ETO估计的唯一标准方法，用于为训练和测试过程提供ANN目标。关于模型，需要很少的气候输入，该模型提供了较低的相关误差比目前现有的基于温度的模型，只考虑L…
This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration ( ETo ) as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ETo prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ETo prediction, proposed by the FAO as the sole standard method for ETo estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider l...