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

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1961))

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Abstract

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 sub-networks 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 STA-Net, 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%.

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Acknowledgment

This work is funded by the National Natural Science Foundation of China (No. 42265010, No. 62162053, No. 62062059, No. 62166032), Natural Science Foundation of Qinghai Province (No. 2023-ZJ-906M), Youth Scientific Research Foundation of Qinghai University (No. 2022-QGY-6) and the Open Project of State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (No. 2020-ZZ-03).

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Authors and Affiliations

  1. Department of Computer Technology and Applications, Qinghai University, No. 251, Ningda Road, Xining, 810016, Qinghai, China

    Tianrui Hou, Li Wu, Xiaoying Wang & Jianqiang Huang

  2. Research and Development Department, Beijing PRESKY Co., Ltd., Beijing, China

    Xinzhong Zhang

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  1. Tianrui Hou
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  2. Li Wu
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  3. Xinzhong Zhang
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  4. Xiaoying Wang
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  5. Jianqiang Huang
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Corresponding author

Correspondence to Li Wu .

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Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Hou, T., Wu, L., Zhang, X., Wang, X., Huang, J. (2024). STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_3

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