Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models

This paper evaluates the performances of mean diurnal variation (MDV), nonlinear regression (NR), lookup tables (LUTs), support vector regression (SVR), k-nearest neighbors (KNNs), gradient boosting (XGBoost), long short-term memory (LSTM), gated recurrent units (GRUs), and the Transformer model with a deep self-attention mechanism to interpolate the turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau. Results indicated that the Transformer model outperformed the other methods that were tested. To further enhance the interpolation accuracy, a combined model of Transformer and a convolutional neural network (CNN), termed Transformer_CNN, was proposed. Herein, while Transformer focused primarily on global attention, the convolution operations in the CNN provided the model with local attention. Experimental outcomes revealed that the interpolations from Transformer_CNN surpassed the traditional single artificial intelligence model approaches. The coefficient of determination (R2) reached 0.95 in the sensible heat flux test set and 0.90 in the latent heat flux test set, thereby confirming the applicability of the Transformer_CNN model for data interpolation of turbulent heat flux on the Tibetan Plateau. Ultimately, the turbulent heat flux observational database from 2007 to 2016 at the station was imputed using the Transformer_CNN model.