Advertisement

Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, China, using principal component analysis and an improved fuzzy C-means algorithm

  • Mingyuan Shi
  • Jianping ChenEmail author
  • Yang Song
  • Wen Zhang
  • Shengyuan Song
  • Xudong Zhang
Original Paper

Abstract

Susceptibility analysis is important in any study of debris flows. Unlike debris flows in Southwest China, debris flows in Northern China occur with different characteristics and much lower frequency. However, neglecting the danger of possible debris flows in this northern area may result in a devastating disaster. In this paper, 12 debris flow catchments located near Zhirui town, in Heshigten Banner, Inner Mongolia, China, were investigated. A geographic information system, a global positioning system, and remote sensing were used to determine geological, topographical, morphological, and vegetation factors of influence. Principal component analysis was carried out to convert this set of possibly correlated factors into a set of values of linearly uncorrelated principal components. The accumulative contribution rate of the first five principal components retained most of the information on these factors, accounting for 90.9 %. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility of debris flows in this area. This method is based on a quantum-behaved particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for four of the debris flow catchments were high, six were moderate, and two were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.

Keywords

Debris flow susceptibility Principal component analysis Quantum-behaved particle swarm optimization algorithm Fuzzy C-means algorithm Debris flow catchments 

Notes

Acknowledgments

This work was supported by the State Key Program of National Natural Science of China (Grant No. 41330636).

References

  1. Avanzi GD, Giannecchini R, Puccinelli A (2004) The influence of the geological and geomorphological settings on shallow landslides. An example in a temperate climate environment: the June 19, 1996 event in north western Tuscany (Italy). Eng Geol 73:215–228CrossRefGoogle Scholar
  2. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New YorkCrossRefGoogle Scholar
  3. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy C-means clustering algorithm. Comput Geosci 10(2–3):191–203CrossRefGoogle Scholar
  4. Carrara A, Crosta G, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94:353–378CrossRefGoogle Scholar
  5. Crosta GB, Frattini P (2003) Distributed modelling of shallow landslides triggered by intense rainfall. Nat Hazard Earth Syst 3(1/2):81–93CrossRefGoogle Scholar
  6. de Scally FA, Owens IF, Louis J (2010) Controls on fan depositional processes in the schist ranges of the Southern Alps, New Zealand, and implications for debris-flow hazard assessment. Geomorphology 122(1–2):99–116CrossRefGoogle Scholar
  7. Dong JJ, Lee CT, Tung YH, Liu CN, Lin KP, Lee JF (2009) The role of the sediment budget in understanding debris flow susceptibility. Earth Surf Process Landform 34:1612–1624CrossRefGoogle Scholar
  8. Farzi S, Dastjerdi AB (2010) Leaf constrained minimal spanning trees solved by modified quantum-behaved particle swarm optimization. Artif Intell Rev 34(1):1–17CrossRefGoogle Scholar
  9. Giannecchini R, Naldini D, Avanzi GD, Puccinelli A (2007) Modelling of the initiation of rainfall induced debris flows in the Cardoso basin (Apuan Alps, Italy). Quat Int 171–172:108–117CrossRefGoogle Scholar
  10. Gokceoglu C, Tunusluoglu MC, Gorum T, Tur H, Gokasan E, Tekkeli AB, Batuk F, Alp H (2009) Description of dynamics of the Tuzla Landslide and its implications for further landslides in the northern slope and shelf of the Cinarcik Basin (Marmara Sea, Turkey). Eng Geol 106(3–4):133–153CrossRefGoogle Scholar
  11. Goktepe AB, Altun S, Sezer A (2005) Soil clustering by fuzzy C-means algorithm. Adv Eng Softw 36(10):691–698CrossRefGoogle Scholar
  12. Heisenberg W (1927) Über den anschaulichen Inhalt der quantentheoretischen kinematik und mechanik. Zeitschrift für Physik 43(3–4):172–198CrossRefGoogle Scholar
  13. Hu W, Xu Q, Rui C, Huang RQ, van Asch TWJ, Zhu X, Xu QQ (2015) An instrumented flume to investigate the initiation mechanism of the post-earthquake huge debris flow in the southwest of China. Bull Eng Geol Environ 74(2):393–404CrossRefGoogle Scholar
  14. Iverson RM (1997) The physics of debris flows. Rev Geophys 35:245–296CrossRefGoogle Scholar
  15. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkGoogle Scholar
  16. Kennedy J, Eberthart RC (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks. Perth, Australia, pp 1942–1948CrossRefGoogle Scholar
  17. Lee S, Chwae U, Min K (2002) Landslide susceptibility mapping by correlation between topography and geological structure: the Janghun area, Korea. Geomorphology 46:149–162CrossRefGoogle Scholar
  18. Li QM, Dehler SA (2015) Inverse spatial principal component analysis for geophysical survey data interpolation. J Appl Geophys 115:79–91CrossRefGoogle Scholar
  19. Li YY, Wang Q, Chen JP, Xu LM, Song SY (2015) K-means algorithm based on particle swarm optimization for identification of rock discontinuity sets. Rock Mech Rock Eng 48(1):375–385CrossRefGoogle Scholar
  20. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30:83–92Google Scholar
  21. Ni HY, Zheng WM, Li ZL, Ba RJ (2010) Recent catastrophic debris flows in Luding county, SW China: geological hazards, rainfall analysis and dynamic characteristics. Nat Hazards 55(2):523–542CrossRefGoogle Scholar
  22. Niu CC, Wang Q, Chen JP, Wang K, Zhang W, Zhou FJ (2014) Debris-flow hazard assessment based on stepwise discriminant analysis and extension theory. Q J Eng Geol Hydrogeol 47:211–222. doi: 10.1144/qjegh2013-038
  23. Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci UK 66:20–30. doi: 10.1016/j.cageo.2013.12.016 CrossRefGoogle Scholar
  24. Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy C-means model. IEEE T Fuzzy Syst 3(3):370–379CrossRefGoogle Scholar
  25. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  26. Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177CrossRefGoogle Scholar
  27. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219CrossRefGoogle Scholar
  28. Sezer EA, Nefeslioglu HA, Gokceoglu C (2014) An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models. Appl Soft Comput 24:126–134CrossRefGoogle Scholar
  29. Shen CW, Lo WC, Chen CY (2012) Evaluating susceptibility of debris flow hazard using multivariate statistical analysis in Hualien County. Disaster Adv 5:743–755Google Scholar
  30. Sun J, Xu WB, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: Cybernetics and intelligent systems proceedings of the 2004 IEEE conference, pp 111–116Google Scholar
  31. Tian N, Lai CH (2014) Parallel quantum-behaved particle swarm optimization. Int J Mach Cyber 5(2):309–318CrossRefGoogle Scholar
  32. Wan S, Lei TC, Huang PC, Chou TY (2008) The knowledge rules of debris flow event: a case study for investigation Chen Yu Lan River, Taiwan. Eng Geol 98(3–4):102–114CrossRefGoogle Scholar
  33. Zhang W, Li HZ, Chen JP, Zhang C, Xu LM, Sang WF (2011) Comprehensive hazard assessment and protection of debris flows along Jinsha River close to the Wudongde dam site in China. Nat Hazard 58:459–477CrossRefGoogle Scholar
  34. Zhang W, Chen JP, Wang Q, An YK, Qian X, Xiang LJ, He LX (2013) Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods. Nat Hazards 66(2):1073–1100CrossRefGoogle Scholar
  35. Zhang W, Chen JP, Wang Q, Yan JY, Guan S, Chen XY, Wang FF (2014) Velocity and runout determination of a debris flow based on energy conservation: the Dongwopu debris flow in Tianjin 48:5–14. Q J Eng Geol Hydrogeol, China. doi: 10.1144/qjegh2014-036 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mingyuan Shi
    • 1
  • Jianping Chen
    • 1
    Email author
  • Yang Song
    • 2
  • Wen Zhang
    • 1
  • Shengyuan Song
    • 1
  • Xudong Zhang
    • 1
  1. 1.College of Construction Engineering, Jilin UniversityChangchunChina
  2. 2.Department of Dermatology and VenereologyFirst Hospital of Jilin UniversityChangchunChina

Personalised recommendations