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首页> 外文期刊>Irrigation Science >Comparative study of Hargreaves's and artificial neural network's methodologies in estimating reference evapotranspiration in a semiarid environment
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Comparative study of Hargreaves's and artificial neural network's methodologies in estimating reference evapotranspiration in a semiarid environment

机译：半干旱环境中Hargreaves法和人工神经网络法估算参考蒸散量方法的比较研究

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摘要

The Penman-Monteith equation (PM) is widely recommended because of its detailed theoretical base. This method is recommended by FAO as the sole method to calculate reference evapotranspiration (ETo) and for evaluating other methods. However, the detailed climatological data required by the Penman-Monteith equation are not often available especially in developing nations. Hargreaves equation (HG) has been successfully used in some locations for estimating ETo where sufficient data were not available to use PM method. The HG equation requires only maximum and minimum air temperature data that are usually available at most weather stations worldwide. Another method used to estimate ETo is the artificial neural network (ANN). Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to compare HG and ANN methods for estimating ETo only on the basis of the temperature data. The 12 weather stations selected for this study are located in Khuzestan plain (southwest of Iran). The HG method mostly underestimated or overestimated ETo obtained by the PM method. The ANN method predicted ETo better than HG method at all sites.
机译：由于其详细的理论基础，因此广泛推荐使用Penman-Monteith方程（PM）。粮农组织建议将此方法作为计算参考蒸散量（ETo）和评估其他方法的唯一方法。但是，Penman-Monteith方程所需的详细气候资料并不经常获得，特别是在发展中国家。 Hargreaves方程（HG）已成功用于某些地点，用于估算没有足够数据可用于PM方法的ETo。 HG方程仅需要全球大多数气象站通常可获得的最高和最低气温数据。用于估计ETo的另一种方法是人工神经网络（ANN）。人工神经网络（ANN）是建模非线性系统的有效工具，所需的输入较少。这项研究的目的是仅基于温度数据比较HG和ANN估计ETo的方法。为本研究选择的12个气象站位于胡舒斯坦平原（伊朗西南部）。 HG方法大部分是通过PM方法获得的ETo被低估或高估了。在所有站点上，ANN方法预测的ETo均优于HG方法。

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