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STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network

EasyChair Preprint no. 10785

25 pagesDate: August 27, 2023


Reconstructing the missing meteorological site temperature data is of great significance for analyzing climate change and predicting related natural disasters, but is a trickily and urgently solved problem. In the past, various interpolation methods were used to solve this problem, but these methods basically ignored the temporal correlation of the site itself. Recently, the methods based on machine learning have been widely studied to solve this problem. However, these methods tend to handle the missing value situation of single site, neglecting spatial correlation between sites. Hence, we put forward a new spatiotemporal attention neural network (STA-Net) for reconstructing missing data in multiple meteorological sites. The STA-Net utilizes the currently state-of-the-art encoder-decoder deep learning architecture and is composed of two subnetworks which include local spatial attention mechanism (LSAM) and multidimensional temporal self-attention mechanism (MTSAM), respectively. Moreover, a multiple-meteorological-site data processing method is developed to generate matrix datasets containing spatiotemporal information so the STA-Net can be trained and tested. To evaluate the STANet, a large number of experiments on real Tibet and Qamdo datasets with the missing rates of 25%, 50% and 75%, respectively, are conducted, meanwhile compared with U-Net, PConvU-Net and BiLSTM. Experimental results have showed that our data processing method is effective and meantime and our STA-Net achieves greater reconstruction effect. In the case with the missing rate of 25% on Tibet test datasets and compared to the other three methods, the MAE declines by 60.21%, 36.42% and 12.70%; the RMSE declines by 56.28%, 32.03% and 14.17%; the R2 increases by 0.75%, 0.20% and 0.07%.

Keyphrases: Attention Mechanism, deep learning, Meteorological station data, missing data imputation, neural networks, Time Series.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Tianrui Hou and Li Wu and Xinzhong Zhang and Xiaoying Wang and Jianqiang Huang},
  title = {STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network},
  howpublished = {EasyChair Preprint no. 10785},

  year = {EasyChair, 2023}}
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