Fill the Gaps of Eddy Covariance Fluxes Using Machine Learning Algorithms [机器学习算法对涡动相关缺失通量数据的插补研究]

作者：Wang S.; Zhang Y.; Meng X.; Song M.; Shang L.; Su Y.; Li Z.

来源：Plateau Meteorology, 2020, 39(6): 1348-1360.

DOI：10.7522/j.issn.1000-0534.2019.00142

• eddy covariance
• gap-filling
• Machine learning

The eddy covariance long-term measurements commonly include data gaps due to system failures，quality control and quality assurance. In this study，the marginal distribution sampling（MDS）algorithm and three machine learning algorithms（random forest RF，support vector machine SVM and artificial neural networks ANN）were applied to fill the gaps of sensible heat flux（H），latent heat flux（LE）and net ecosystem exchange in 2016 over an alpine ecosystem. Results indicate that the performance of RF is better than SVM and ANN. During the nighttime，the periods of sunrise and sunset，and in the winter and spring，the performance of three machine learning algorithms is relatively weak，compared to other periods or seasons. On the monthly and annual scales，the filled NEE budget is significantly influenced by the choice of gap-filling method，compared to H and LE.

• 单位
  中国科学院; 中国科学院大学