Preprints
https://doi.org/10.5194/egusphere-2023-2685
https://doi.org/10.5194/egusphere-2023-2685
08 Jan 2024
 | 08 Jan 2024
Status: this preprint is open for discussion.

A Study on the Transformer-CNN Imputation Method for Turbulent Heat Flux Dataset in the Qinghai-Tibet Plateau Grassland

Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu

Abstract. Based on the turbulent heat flux from the third scientific expedition to the Qinghai-Tibet Plateau in 2012, imputation evaluations were conducted using algorithms like Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model with deep self-attention mechanism. Results indicated that the Transformer model performed optimally. To further enhance imputation accuracy, a combined model of Transformer and Convolutional Neural Network (CNN), termed as Transformer_CNN, was proposed. Herein, while the Transformer primarily focused on global attention, the convolution operations in the CNN provided the model with local attention. Experimental outcomes revealed that the imputations from Transformer_CNN surpassed the traditional single artificial intelligence model approaches. The coefficient of determination (R2) reached 0.949 in the sensible heat flux test set and 0.894 in the latent heat flux test set, thereby confirming the applicability of the Transformer_CNN model for data imputation of turbulent heat flux in the Qinghai-Tibet Plateau. Ultimately, the turbulent heat flux observational database from 2007 to 2016 at the station was imputed using the Transformer_CNN model.

Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu

Status: open (until 04 Mar 2024)

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  • RC1: 'Comment on egusphere-2023-2685', Anonymous Referee #1, 07 Feb 2024 reply
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Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu

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Short summary
This study assesses turbulent heat flux data imputation at the Qinghai-Tibet Plateau using various machine learning models. The Transformer model emerged as the most effective, leading to the creation of the Transformer_CNN model, which integrates global and local attention mechanisms. Experimental results showed that Transformer_CNN surpassed other models in performance. This model was effectively used to impute the station's heat flux data from 2007 to 2016.