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农田下垫面近地层湍流通量的机器学习订正个例研究

A Case Study on Machine Learning Correction of Surface Layer Turbulent Fluxes over a Farmland

  • 摘要: 近地层湍流通量是表征地-气相互作用的重要变量,在梯度观测或大气数值模式中通常基于Monin-Obukhov相似理论(MOST)进行计算。为进一步提升近地层湍流通量的计算精度,本文以涡动相关法得到的湍流通量作为参考值,使用随机森林和XGBoost对MOST得到的湍流通量进行了订正,显著提高了特征温度和特征比湿的计算精度。为精简输入变量,本文进一步对输入变量进行了前向、后向变量选择,经变量选择后的最佳输入变量组合仍保持了较高的计算精度。此外,将选择后的输入变量组合应用到另一个机器学习模型(人工神经网络)时,其计算精度也在MOST结果的基础上取得了显著提高,可见该变量选择方法可有效精简机器学习所需要的输入变量。

     

    Abstract: Turbulent fluxes in the surface layer are significant variables that characterize the interaction between the land and the atmosphere, which are typically calculated based on the Monin-Obukhov Similarity Theory (MOST) in gradient observations or atmospheric numerical models. To further enhance the calculational accuracy of turbulent fluxes in the surface layer, in this paper, the turbulent fluxes derived from the MOST are corrected by Random Forest and XGBoost, with fluxes obtained by the eddy covariance method as reference values, which significantly improves the calculational accuracy of the temperature scale and moisture scale. In order to reduce the number of input variables, this study further performs forward and backward variable selection on the input variables The optimal combinations of input variables after selecting still maintain a high level of computational accuracy. Furthermore, when the selected combinations of input variables are applied to another machine learning model, the artificial neural network, their computational accuracy also show a significant improvement in comparison with the results of MOST. This demonstrates that the variable selection method in this paper of input variables required for machine learning method.

     

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