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首页> 外文期刊>Water Resources Management >Comparison of M5 Model Tree and Artificial Neural Network's Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images
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Comparison of M5 Model Tree and Artificial Neural Network's Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images

机译：M5模型树与人工神经网络在NOAA卫星图像每日参考蒸发量建模中的方法比较

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摘要

The objective of this study was to compare feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET0) only on the basis of the remote sensing based surface temperature (T-s) data. The input variables for these models were the daytime surface temperature at the cold pixel obtained from the AVHRR/NOAA sensor and extraterrestrial radiation (R-a). The study has been carried out in five irrigated units that cultivate sugar cane, which located in the Khuzestan plain in the southwest of Iran. A total of 663 images of NOAA-AVHRR level 1b during the period 1999-2009, covering the area of this study were collected from the Satellite Active Archive of NOAA. The FAO-56 Penman-Monteith model was used as a reference model for assessing the performance of the two above approaches. The study demonstrated that modelling of ET0 through the use of M5 model tree gave better estimates than the ANN technique. However, differences with the ANN model are small. Root mean square error and R-2 for the comparison between reference and estimated ET0 for the tested data set using the proposed M5 model are 13.7 % and 0.96, respectively. For the ANN model these values are 14.3 % and 0.95, respectively.
机译：本研究的目的是仅基于基于遥感的地表温度（T-s）数据来比较前馈人工神经网络（ANN）和M5模型树，以估计参考蒸散量（ET0）。这些模型的输入变量是从AVHRR / NOAA传感器获得的冷像素的白天表面温度和地外辐射（R-a）。这项研究是在位于伊朗西南部Khuzestan平原的五个种植甘蔗的灌溉单位中进行的。从NOAA卫星活动档案库中收集了本研究范围内1999-2009年期间的663幅NOAA-AVHRR 1b级图像。 FAO-56 Penman-Monteith模型被用作评估上述两种方法的绩效的参考模型。该研究表明，通过使用M5模型树对ET0进行建模比使用ANN技术可以提供更好的估计。但是，与ANN模型的差异很小。使用提议的M5模型对测试数据集的参考ET0和估计ET0进行比较的均方根误差和R-2分别为13.7％和0.96。对于ANN模型，这些值分别为14.3％和0.95。

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