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Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin

Received: 29 September 2021     Accepted: 20 October 2021     Published: 29 October 2021
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Abstract

In recent years, due to the obvious ground settlement and other phenomena of the Yinxi Industrial Park in Baiyin, it has brought many hidden dangers to the local development, it is of great practical significance to monitor the deformation of the area for a long time series. The ground deformation field of Yinxi Industrial Park from June 2018 to April 2021 was obtained by processing Sentinel-1A data using SBAS technology, and the high coherence point D1 was predicted and analyzed by BP neural network. The results show that subsidence occurs in several places in the Yinxi Industrial Park, and the average annual subsidence rate ranges from -19.28 mm to 5.08 mm, the areas of severe settlement have a clear geographical distribution, mainly concentrated in road and building areas, other areas have a more stable ground base; the mean square error in the BP neural network prediction result is 2.56 mm, and the average relative error is 6.06%, which is a high prediction accuracy. The predicted cumulative settlement value at point D1 in 2023 is 45 mm, and there is a tendency for the settlement to intensify. The prediction results are of great significance for the early identification and prevention of ground settlement in the study area.

Published in American Journal of Civil Engineering (Volume 9, Issue 5)
DOI 10.11648/j.ajce.20210905.13
Page(s) 167-172
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

SBAS Technology, Land Subsidence, BP Neural Network, Yinxi Industrial Park

References
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[7] F. M Zhao, Y Zhang, X. M Meng, et al. Early identification of geological hazards in the Gaizi valley near the Karakoran Highway based on SBAS-InSAR technology [J]. Hydrogeology & Engineering Geology, 2020, 47 (01): 142-152.
[8] G. Y Pan, Q. X Tao, Y Chen, et al. Monitoring and analysis of sedimentation in Jiyang mining area of Shandong Province based on SBAS-InSAR [J]. The Chinese Journal of Geological Hazard and Control, 2020, 31 (04): 100-106+120.
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  • APA Style

    Hui Zhang, Xinghai Dang, Liqi Jia, Jianyun Zhao, Ming Lu. (2021). Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin. American Journal of Civil Engineering, 9(5), 167-172. https://doi.org/10.11648/j.ajce.20210905.13

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    ACS Style

    Hui Zhang; Xinghai Dang; Liqi Jia; Jianyun Zhao; Ming Lu. Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin. Am. J. Civ. Eng. 2021, 9(5), 167-172. doi: 10.11648/j.ajce.20210905.13

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    AMA Style

    Hui Zhang, Xinghai Dang, Liqi Jia, Jianyun Zhao, Ming Lu. Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin. Am J Civ Eng. 2021;9(5):167-172. doi: 10.11648/j.ajce.20210905.13

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  • @article{10.11648/j.ajce.20210905.13,
      author = {Hui Zhang and Xinghai Dang and Liqi Jia and Jianyun Zhao and Ming Lu},
      title = {Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin},
      journal = {American Journal of Civil Engineering},
      volume = {9},
      number = {5},
      pages = {167-172},
      doi = {10.11648/j.ajce.20210905.13},
      url = {https://doi.org/10.11648/j.ajce.20210905.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20210905.13},
      abstract = {In recent years, due to the obvious ground settlement and other phenomena of the Yinxi Industrial Park in Baiyin, it has brought many hidden dangers to the local development, it is of great practical significance to monitor the deformation of the area for a long time series. The ground deformation field of Yinxi Industrial Park from June 2018 to April 2021 was obtained by processing Sentinel-1A data using SBAS technology, and the high coherence point D1 was predicted and analyzed by BP neural network. The results show that subsidence occurs in several places in the Yinxi Industrial Park, and the average annual subsidence rate ranges from -19.28 mm to 5.08 mm, the areas of severe settlement have a clear geographical distribution, mainly concentrated in road and building areas, other areas have a more stable ground base; the mean square error in the BP neural network prediction result is 2.56 mm, and the average relative error is 6.06%, which is a high prediction accuracy. The predicted cumulative settlement value at point D1 in 2023 is 45 mm, and there is a tendency for the settlement to intensify. The prediction results are of great significance for the early identification and prevention of ground settlement in the study area.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin
    AU  - Hui Zhang
    AU  - Xinghai Dang
    AU  - Liqi Jia
    AU  - Jianyun Zhao
    AU  - Ming Lu
    Y1  - 2021/10/29
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajce.20210905.13
    DO  - 10.11648/j.ajce.20210905.13
    T2  - American Journal of Civil Engineering
    JF  - American Journal of Civil Engineering
    JO  - American Journal of Civil Engineering
    SP  - 167
    EP  - 172
    PB  - Science Publishing Group
    SN  - 2330-8737
    UR  - https://doi.org/10.11648/j.ajce.20210905.13
    AB  - In recent years, due to the obvious ground settlement and other phenomena of the Yinxi Industrial Park in Baiyin, it has brought many hidden dangers to the local development, it is of great practical significance to monitor the deformation of the area for a long time series. The ground deformation field of Yinxi Industrial Park from June 2018 to April 2021 was obtained by processing Sentinel-1A data using SBAS technology, and the high coherence point D1 was predicted and analyzed by BP neural network. The results show that subsidence occurs in several places in the Yinxi Industrial Park, and the average annual subsidence rate ranges from -19.28 mm to 5.08 mm, the areas of severe settlement have a clear geographical distribution, mainly concentrated in road and building areas, other areas have a more stable ground base; the mean square error in the BP neural network prediction result is 2.56 mm, and the average relative error is 6.06%, which is a high prediction accuracy. The predicted cumulative settlement value at point D1 in 2023 is 45 mm, and there is a tendency for the settlement to intensify. The prediction results are of great significance for the early identification and prevention of ground settlement in the study area.
    VL  - 9
    IS  - 5
    ER  - 

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Author Information
  • School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China

  • School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China

  • School of Design Arts, Lanzhou University of Technology, Lanzhou, China

  • Department of Geologic Engineering, Qinghai University, Xining, China

  • School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China

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