Intelligent Prediction of Surface Turbulent Fluxes: An Innovative Approach Based on the iTransformer Model

Abstract Surface turbulent fluxes constitute key energy exchanges in the atmospheric boundary layer (ABL). Accurate prediction of variations in the ABL is essential for agricultural ecology and climate studies. Existing prediction methods include those based on Monin–Obukhov similarity theory (MOST) and machine learning (ML). However, the MOST method requires experimental parameters and empirical equations, while the ML method considerably relies on manual feature extraction. Given the potential of deep learning (DL) in time series prediction, an inverted Transformer (iTransformer) model is employed in this study to predict friction velocity, kinematic sensible heat flux, and kinematic latent heat flux values across different seasons. The iTransformer model encodes the data via transposed encoding, and a multivariate self‐attention module is employed to capture the correlations between variables. The feed‐forward neural networks leverage these correlations to predict surface turbulent fluxes. Compared with other methods, including the Transformer and ML methods, the iTransformer model can not only improve the prediction correlations but also reduce the errors in surface turbulent fluxes. Moreover, the model can effectively capture the trends in various fluxes within 1 month or even one day. In summary, the iTransformer model can significantly increase the predictive performance for surface turbulent fluxes.