Advertisement

Correlation Analysis Between Deep Displacement and Multi-source Landslide Monitoring Data

  • Genwang Li
  • Fuyang KeEmail author
Conference paper
  • 20 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1227)

Abstract

The landslide geological disasters have many hazard factors, and the data indicators for monitoring geological disasters are not the same. Through the use of various intelligent sensors, multi-angle and multi-source monitoring of landslide geological disasters can be carried out. The deep displacement is an important parameter to reflect the deformation depth and range of the landslide body. The surface displacement, groundwater level and pore water pressure can all react to the stability of the landslide body. In this paper, the experimental data can be used to study the correlation between different depths of deep displacement and surface displacement, groundwater level and pore water pressure. First, qualitative research is carried out in the form of graphs; then, quantitative research is carried out by using Pearson correlation coefficient method using MATLAB software; finally, the model is established by multivariate linear regression method. Through comparative analysis, it can be found that the established multivariate linear regression model can well predict surface displacement, groundwater level, pore water pressure and deep displacement.

Keywords

Deep displacement Multi-source data Landslide monitoring Correlation analysis 

Notes

Acknowledgements

This study is supported by the National Natural Science Foundation of China (grant no. 41674036).

References

  1. 1.
    Xue, J., Xu, J., Zhang, F., et al.: Study on meteorological forecast methods for regional geological hazards. Meteorology 31(10), 24–27 (2005)Google Scholar
  2. 2.
    Chen, W., Xu, Q.: Research on early warning benchmark of geological disaster rainfall. Earth Environ. 39(3), 393–397 (2011)Google Scholar
  3. 3.
    Xiao, W., Huang, D., Li, H., et al.: Research on meteorological forecasting and early warning methods for geological disasters. Geol. Resour. 14(4), 274–278 (2005)Google Scholar
  4. 4.
    Wei, F., Hu, K., Cui, P., et al.: Decision support system for debris flow mitigation in mountainous cities. J. Nat. Disasters 11(2), 31–36 (2002)Google Scholar
  5. 5.
    Yue, J., Tu, B., Liu, G., et al.: Application of geological disaster warning and forecasting and information management system. Nat. Disaster Rep. 17(6), 60–63 (2008)Google Scholar
  6. 6.
    Liu, S., Qiang, F., Nie, S.: Correlation analysis of geological disaster distribution and influencing factors in Jixian County. J. Water Resour. Archit. Eng. 15(01), 165–170 (2017)Google Scholar
  7. 7.
    Qiang, F., Zhao, F., Dang, Y.: Correlation analysis of geological hazards and influencing factors in Qinba mountain area of Southern Shaanxi. South-to-North Water Transf. Water Sci. Technol. 13(03), 557–562 (2015)Google Scholar
  8. 8.
    Yang, Q., Yao, C., Liu, S., Gao, F.: Analysis of the relationship between geological disaster development and topography in mountainous areas of Shandong Province. Chin. J. Geol. Hazard Control 26(02), 93–96 (2015)Google Scholar
  9. 9.
    Zeng, W., Zhang, Z.: Development characteristics and formation conditions of geological disasters in Suining County. Geol. Disasters Environ. Prot. Environ. 29(01), 17–22 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Jiangsu Kebo Space Information Technology Co., Ltd.NanjingChina
  2. 2.School of Remote Sensing and Geomatics EngineeringNanjing University Information of Science and TechnologyNanjingChina

Personalised recommendations