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

Hui Zhang; Xinghai Dang; Liqi Jia; Jianyun Zhao; Ming Lu

doi:doi:10.11648/j.ajce.20210905.13

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

Hui Zhang, Xinghai Dang, Liqi Jia, Jianyun Zhao, Ming Lu

Published in American Journal of Civil Engineering (Volume 9, Issue 5)

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), 2024. 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

Hui Zhang

School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China
Xinghai Dang

School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China
Liqi Jia

School of Design Arts, Lanzhou University of Technology, Lanzhou, China
Jianyun Zhao

Department of Geologic Engineering, Qinghai University, Xining, China
Ming Lu

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

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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}
}

Copy | Download

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  -

Copy | Download