A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

  • Zhiyong Wu
  • Yanli Wu
  • Yitian Yang
  • Fuwei Chen
  • Na Zhang
  • Yutian Ke
  • Wenping Li
Original Paper

Abstract

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.

Keywords

Landslide Susceptibility Logistic regression Statistical index China 

Notes

Acknowledgments

The authors thank the National Basic Research Program of China “973” (No. 2015CB251601), the People’s Livelihood Research Project of Hebei Province (201301211), and the Research Fund for the Scientific Studies in Higher Education Institutions of Hebei Province (QN2015306) for the financial support. Also, the authors would like to express their gratitude to the anonymous reviewers for their constructive comments and suggestions, which highly increased the quality of the paper.

References

  1. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34CrossRefGoogle Scholar
  2. Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  3. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrol Sci J 24(1):43–69Google Scholar
  4. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 1–18Google Scholar
  5. Chen Z, Wang J (2007) Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Nat Hazards 42(1):75–89CrossRefGoogle Scholar
  6. Chen W, Li X, Wang Y, Chen G, Liu S (2014) Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the three gorges, China. Remote Sens Environ 152:291–301CrossRefGoogle Scholar
  7. Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental Earth Sciences 63(2):397–406CrossRefGoogle Scholar
  8. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC et al (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards 65(1):135–165CrossRefGoogle Scholar
  9. Dragićević S, Lai T, Balram S (2015) GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International 45:114–125CrossRefGoogle Scholar
  10. Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2):175–189CrossRefGoogle Scholar
  11. Foumelis M, Lekkas E, Parcharidis I (2004) Landslide susceptibility mapping by GIS-based qualitative weighting procedure in Corinth area. Bulletin of the Geological Society of Greece XXXVI, 904–912. Proceedings of the 10th international congress, Thessaloniki, April 2004Google Scholar
  12. Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens Environ 51(1):138–156CrossRefGoogle Scholar
  13. Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926CrossRefGoogle Scholar
  14. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3):347–366CrossRefGoogle Scholar
  15. Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59(3):1491–1512CrossRefGoogle Scholar
  16. Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439CrossRefGoogle Scholar
  17. Kavzoglu T, Sahin EK, Colkesen I (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Hazards 76(1):471–496CrossRefGoogle Scholar
  18. Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4(4):327–338CrossRefGoogle Scholar
  19. Liu M, Chen X, Yang S (2014) Collapse Landslide and Mudslides Hazard Zonation. In Landslide Science for a Safer Geoenvironment (pp. 457–462). Springer International PublishingGoogle Scholar
  20. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236CrossRefGoogle Scholar
  21. Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Sci Soc Am J 50(5):1294–1298CrossRefGoogle Scholar
  22. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30CrossRefGoogle Scholar
  23. Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71(1):523–547CrossRefGoogle Scholar
  24. Oh HJ, Lee S, Soedradjat GM (2010) Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environmental Earth Sciences 60(6):1317–1328CrossRefGoogle Scholar
  25. Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91(2):117–134CrossRefGoogle Scholar
  26. Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197Google Scholar
  27. Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental earth sciences 68(5):1443–1464CrossRefGoogle Scholar
  28. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996CrossRefGoogle Scholar
  29. Pourghasemi HR, Moradi HR, Aghda SF (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779CrossRefGoogle Scholar
  30. Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing 38(2):301–320CrossRefGoogle Scholar
  31. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences 60(5):1037–1054CrossRefGoogle Scholar
  32. Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115:55–70CrossRefGoogle Scholar
  33. Sharma M, Kumar R (2008) GIS-based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, HP, India. B Eng Geol Environ 67(1): 129–137Google Scholar
  34. Sharma LP, Patel N, Ghose MK, Debnath P (2015) Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India. Nat Hazards 75(2):1555–1576CrossRefGoogle Scholar
  35. Soofastaei A, Aminossadati SM, Arefi MM, Kizil MS (2016) Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption. Int J Min Sci Technol 26(2): 285–293Google Scholar
  36. Stumpf A, Kerle N (2011a) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115(10):2564–2577CrossRefGoogle Scholar
  37. Stumpf A, Kerle N (2011b) Combining random forests and object-oriented analysis for landslide mapping from very high resolution imagery. Procedia Environmental Sciences 3:123–129CrossRefGoogle Scholar
  38. Van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2:73–84Google Scholar
  39. Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414CrossRefGoogle Scholar
  40. Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287CrossRefGoogle Scholar
  41. Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences 61(4):821–836CrossRefGoogle Scholar
  42. Yilmaz C, Topal T, Süzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental earth sciences 65(7):2161–2178CrossRefGoogle Scholar
  43. Youssef AM, Al-Kathery M, Pradhan B (2014) Landslide susceptibility mapping at al-hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19(1):113–134CrossRefGoogle Scholar
  44. Zhang P, Peterson S, Neilans D, Wade S, McGrady R, Pugh J (2016) Geotechnical risk management to prevent coal outburst in room-and-pillar mining. Int J Min Sci Technol 26(1):9–18Google Scholar

Copyright information

© Saudi Society for Geosciences 2017

Authors and Affiliations

  • Zhiyong Wu
    • 1
    • 2
  • Yanli Wu
    • 1
    • 3
  • Yitian Yang
    • 2
  • Fuwei Chen
    • 4
  • Na Zhang
    • 2
  • Yutian Ke
    • 5
  • Wenping Li
    • 1
  1. 1.School of Resources and GeoscienceChina University of Mining and TechnologyXuzhouChina
  2. 2.College of Resource and Environmental SciencesHebei Normal University for NationalitiesChengdeChina
  3. 3.Exploration & Surveying Division, Northwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting GroupXi’anChina
  4. 4.Science Research DepartmentHebei Normal University for NationalitiesChengdeChina
  5. 5.School of Civil Engineering and MechanicsLanzhou UniversityLanzhouChina

Personalised recommendations