Skip to main content
SpringerLink
Log in

Susceptibility zoning of karst geological hazards using machine learning and cloud model

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Accurate assessment on serious geological hazards using artificial intelligence is required in geological hazards assessment with aim to improve the efficiency and accuracy of karst geological hazard identification. In this paper, topography and geology characteristics are selected as susceptibility factors for geological disasters identification. The area of Guibei, Guangxi Province, was selected as the study area because of its typical karst landform. Intelligent classification of influential factors for karst geological hazard identification is realized by utilization of support vector machine, meanwhile, the cloud model theory was introduced and a comprehensive method of susceptibility zoning deployment of geological hazards in karst areas was proposed, further, a quantitative susceptibility assessment model for unsuitable geology in karst areas was established. After that, a comprehensive geological hazard susceptibility index (S) proposed in this paper is calculated with assistance of machine learning theory in the proposed model. The results show that the influential factors can be divided into five grades through statistical learning, the susceptibility index of geological hazards in the study area is between 0.713 and 5.798, the susceptibility of geological hazards in the mid-section take the largest share. Differences in the distribution of high, middle, and low susceptibility zones are significant. Different susceptibility zoning have different influence on engineering construction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Choubin, B., Darabi, H., Rahmati, O., et al.: River suspended sediment modelling using the CART model: a comparative study of machinelearning techniques. Sci. Total Environ. 615(2), 272–281 (2018)

    Article  Google Scholar 

  2. Chapi, K., Singh, V., Shirzadi, A., et al.: A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ. Model. Softw. 95(9), 229–245 (2017)

    Article  Google Scholar 

  3. Johannes, M., Jakob, T., Michael, F., et al.: Learning from the crowd: Road infrastructure monitoring system. J. Traffic Transp. Eng. 4(5), 451–463 (2017)

    Google Scholar 

  4. Waele, J., Gutierrez, F., Audra, P.: Karst geomorphology: from hydrological functioning to palaeoenviron mental reconstructions. Geomorphology 229(229), 1–2 (2015)

    Google Scholar 

  5. Zhou, W., Beck, B.F., Adams, A.L.: Application of matrix analysis in delineating sinkhole risk areas along highway (I-70 near Frederick, Maryland). Environ. Geol. 44(7), 834–842 (2003)

    Article  Google Scholar 

  6. Boni, M., Rollinson, G., Mondillo, N., et al.: Quantitative mineralogical characterization of karst bauxite deposits in the southern Apennines, Italy. Econ. Geol. 108(4), 813–833 (2013)

    Article  Google Scholar 

  7. Bautista, F., Palacio-Aponte, G., Quintana, P., et al.: Spatial distribution and development of soils in tropical karst areas from the Peninsula of Yucatan, Mexico. Geomorphology 135(3), 308–321 (2011)

    Article  Google Scholar 

  8. Mylroie, J.E., Carew, J.L.: Karst development on carbonate islands. Speleogenesis Evol. Karst Aquifers 1(2), 1–21 (2003)

    Google Scholar 

  9. Vesper, R.: Water in karst: management, vulnerability, and restoration. Groundwater 51(5), 656–656 (2013)

    Article  Google Scholar 

  10. Parise, M., Closson, D., Gutiérrez, F., et al.: Anticipating and managing engineering problems in the complex karst environment. Environ. Earth Sci. 74(12), 7823–7835 (2015)

    Article  Google Scholar 

  11. Zhang, Z.L.: Study on karst development pattern of Qianjiang–Zhangjiajie–Changde railway. Railw. Stand. Des. 12, 15–18 (2013)

    Google Scholar 

  12. Shen, Z.P., Sun, H., Wu, B., et al.: Study on typical geologic hazard in karst depressions and its countermeasures. Chin. J. Geol. Hazard Control 27(2), 137–144 (2016)

    Google Scholar 

  13. Nichol, J.E., Shaker, A., Wong, M.S.: Application of high-resolution stereo satellite images to detailed landslide hazard assessment. Geomorphology 76(1), 68–75 (2006)

    Article  Google Scholar 

  14. Tang, C., Zhu, D.K.: Assessment of debris flow risk of Yunnan province by using GIS. Sci. Geogr. Sin. 22(3), 300–304 (2002)

    Google Scholar 

  15. Huang, X.T.: Safety inspection and risk evaluation of geological disasters of high slopes of expressways. Technol. Highw. Transp. 2, 8–11 (2012)

    Google Scholar 

  16. Martinotti, M.E., Pisano, L., Marchesini, l, et al.: Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy. Natu. Hazards Earth Syst. Sci. 17(3), 467–480 (2017)

    Article  Google Scholar 

  17. Qi, H.L., Tian, W.P., Li, J.C.: Regional risk evaluation of flood disasters for the trunk-highway in Shaanxi, China. Int. J. Environ. Res. Public Health 12(11), 13861–13870 (2015)

    Article  Google Scholar 

  18. Jia, X.L., Xu, J.L.: Cloud model-based seismic risk assessment of road in earthquake region. J. Tongji Univ. 42(9), 1352–1358, 1458 (2014)

  19. Dai, J.L., Lei, M.T.: Study on the risk assessment on karst collapse in linear engineering. Carsol. Sin. 31(3), 296–302 (2012)

    Google Scholar 

  20. Li, S.C., Wu, J., Xu, Z.H., et al.: Unascertained measure model of water and mud inrush risk evaluation in karst tunnels and its engineering application. Ksce J. Civ. Eng. 21(4), 1170–1182 (2017)

    Article  Google Scholar 

  21. Xu, Z.H., Li, S.C., et al.: Risk assessment of water or mud inrush of karst tunnels based on analytic hierarchy process. Rock Soil Mech. 32(6), 1757–1766 (2011)

    Google Scholar 

  22. Jia, X.L., Xu, J.L., Yang, H.Z., et al.: Calculation of broken index of surface based on GIS. J. Chongqing Univ. 35(11), 126–130 (2012)

    Google Scholar 

  23. Li, Z., Song, E.Q.: The study at the geological-engineering characters of the imbricate fault in the Hongqiling highway-tunnel. J. Railw. Eng. Soc. 21(4), 31–34 (2004)

    Google Scholar 

  24. Wang, X., Thome, N., Cord, M.: Gaze latent support vector machine for image classification improved by weakly supervised region selection. Pattern Recognit. 72(6), 59–71 (2017)

    Article  Google Scholar 

  25. Li, X., Liu, Y., Wang, Y., et al.: Evaluating transit operator efficiency: an enhanced DEA model with constrained fuzzy-AHP cones. J. Traffic Transp. Eng. 3(3), 215–225 (2016)

    Google Scholar 

  26. Jiao, S.: The algorithm of mean random consistency index in AHP and its implementation. J. Taiyuan Norm. Univ. 5(4), 45–47 (2006)

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the National Key Research and Development Program of China (No. 2016YFC0802208), the Natural Science Foundation of Shaanxi Province (No. 2017JQ5122) and the Fundamental Research Funds for the Central Universities of China (Nos. 310821172201, 310821172202).

Author information

Authors and Affiliations

  1. Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’An, China

    Jia Xingli, Dai Qingmiao & Yang Hongzhi

Authors
  1. Jia Xingli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dai Qingmiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Hongzhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jia Xingli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xingli, J., Qingmiao, D. & Hongzhi, Y. Susceptibility zoning of karst geological hazards using machine learning and cloud model. Cluster Comput 22 (Suppl 4), 8051–8058 (2019). https://doi.org/10.1007/s10586-017-1590-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1590-0

Keywords

Search

Navigation