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A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

Abstract

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.

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References

  1. Ba Q, Chen Y, Deng S, Wu Q, Yang J, Zhang J (2017) An improved information value model based on gray clustering for landslide susceptibility mapping. ISPRS Int J Geo-Inf 6(1):1–20. https://doi.org/10.3390/ijgi6010018

    Article  Google Scholar 

  2. Bhatt BP, Awasthi KD, Heyojoo BP, Silwal T, Kafle G (2013) Using geographic information system and analytical hierarchy process in landslide hazard zonation. Appl Ecol Env Res 1(2):14–22. https://doi.org/10.12691/aees-1-2-1

    Google Scholar 

  3. Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75(3):1–21. https://doi.org/10.1007/s12665-015-5047-6

    Article  Google Scholar 

  4. Carrara A, Guzzetti F (1995) Geographical information systems in assessing natural hazards. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8404-3

    Book  Google Scholar 

  5. Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the Peloponnese peninsula, Greece. Geosciences 4(3):176–190. https://doi.org/10.3390/geosciences4030176

    Article  Google Scholar 

  6. Che VB, Kervyn M, Suh CE, Fontijn K, Ernst GGJ, Marmol MAD (2012) Landslide susceptibility assessment in Limbe (SW Cameroon): a field calibrated seed cell and information value method. Catena 92(1):83–98. https://doi.org/10.1016/j.catena.2011.11.014

    Article  Google Scholar 

  7. Chen J, Yang ST, Li HW, Zhang B, Lv JR (2013) Research on geographical environment unit division based on the method of natural breaks (Jenks). Int Arch Photogramm Remote Sens Spat Inf Sci XL-4/W3(4):47–50. https://doi.org/10.5194/isprsarchives-XL-4-W3-47-2013

    Article  Google Scholar 

  8. Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75(10):1–16. https://doi.org/10.1007/s12665-016-5317-y

    Google Scholar 

  9. Chiessi V, Toti S, Vitale V (2016) Landslide susceptibility assessment using conditional analysis and rare events logistics regression: a case-study in the Antrodoco area (Rieti, Italy). Journal of Geoscience and Environment Protection 4(12):1–21. https://doi.org/10.4236/gep.2016.412001

    Article  Google Scholar 

  10. Chung CF, Fabbri AG (1995) Multivariate regression analysis for landslide hazard zonation. Advances in Natural & Technological Hazards Research 5:107–133. https://doi.org/10.1007/978-94-015-8404-3

    Article  Google Scholar 

  11. Conforti M, Pascale S, Robustelli G, Sdao F (2013) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo river catchment (northern Calabria, Italy). Catena 1:236–250. https://doi.org/10.1016/j.catena.2013.08.006.Onlinefirst

    Google Scholar 

  12. Eeckhaut MVD, Moeyersons J, Nyssen J, Abraha A, Poesen J, Haile M (2009a) Spatial patterns of old, deep-seated landslides: a case-study in the northern Ethiopian highlands. Geomorphology 105(3–4):239–252. https://doi.org/10.1016/j.geomorph.2008.09.027

    Article  Google Scholar 

  13. Eeckhaut MVD, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009b) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9(2):507–521. https://doi.org/10.5194/nhess-9-507-2009

    Article  Google Scholar 

  14. Erener A, Düzgün HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environ Earth Sci 66(3):1–19. https://doi.org/10.1007/s12665-011-1297-0

    Article  Google Scholar 

  15. Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3–4):85–98. https://doi.org/10.1016/j.enggeo.2008.03.022

    Article  Google Scholar 

  16. Garcíarodríguez MJ, Malpica JA (2010) Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an artificial neural network model. Nat Hazards Earth Syst Sci 10(6):1307–1315. https://doi.org/10.5194/nhess-10-1307-2010

    Article  Google Scholar 

  17. Günther A, Eeckhaut MVD, Malet JP, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224(2):69–85. https://doi.org/10.1016/j.geomorph.2014.07.011

    Article  Google Scholar 

  18. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study central Italy. Geomorphology 31(1-4):181–216. https://doi.org/10.1016/S0169-555X(99)00078-1

    Article  Google Scholar 

  19. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y (2015) Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 33(1):1–25. https://doi.org/10.1016/j.mri.2014.09.004

    Article  Google Scholar 

  20. Hölbling D, Friedl B, Eisank C (2015) An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Sci Inform 8(2):327–335. https://doi.org/10.1007/s12145-015-0217-3

    Article  Google Scholar 

  21. Huabin W, Gangjun Weiya LX, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567. https://doi.org/10.1191/0309133305pp462ra

    Article  Google Scholar 

  22. Jia N, Mitani Y, Xie M, Tong J, Yang Z (2015) GIS deterministic model-based 3d large-scale artificial slope stability analysis along a highway using a new slope unit division method. Nat Hazards 76(2):873–890. https://doi.org/10.1007/s11069-014-1524-6

    Article  Google Scholar 

  23. 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(3–4):347–366. https://doi.org/10.1016/j.enggeo.2006.03.004

    Article  Google Scholar 

  24. Kirschbaum DB, Stanley T, Simmons J (2015) A dynamic landslide hazard assessment system for central America and Hispaniola. Nat Hazards Earth Syst Sci 3(4):2847–2882. https://doi.org/10.5194/nhessd-3-2847-2015

    Article  Google Scholar 

  25. Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine Slovenia. Geomorphology 74(1–4):17–28. https://doi.org/10.1016/j.geomorph.2005.07.005

    Article  Google Scholar 

  26. Kouli M, Loupasakis C, Soupios P, Vallianatos F (2009) Landslide hazard zonation in high risk areas of Rethymno prefecture, Crete Island, Greece. Nat Hazards 52(3):599–621. https://doi.org/10.1007/s11069-009-9403-2

    Article  Google Scholar 

  27. Kreuzer MT, Wilde M, Terhorst B, Damm B (2017) A landslide inventory system as a base for automated process and risk analyses. Earth Sci Inform 10(4):1–9. https://doi.org/10.1007/s12145-017-0307-5

    Article  Google Scholar 

  28. Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34(2):223–232. https://doi.org/10.1007/s00267-003-0077-3

    Article  Google Scholar 

  29. Lee JH, Park HJ (2016) Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach. Landslides 13(5):885–903. https://doi.org/10.1007/s10346-015-0646-6

    Article  Google Scholar 

  30. Li C, Wu S, Zhu Z (2014) The assessment of submarine slope instability in Baiyun sag using gray clustering method. Nat Hazards 74(2):1179–1190. https://doi.org/10.1007/s11069-014-1241-1

    Article  Google Scholar 

  31. Lundgren L (1978) Studies of soil and vegetation development on fresh landslide scars in the Mgeta Valley, western Uluguru Mountains, Tanzania. Geogr Ann 60(3/4):91–127. https://doi.org/10.2307/520435

    Article  Google Scholar 

  32. Meijerink AMJ (1988) Data acquisition and data capture through terrain mapping units. Int Comput J 1:23–44

    Google Scholar 

  33. Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81(1):254–263. https://doi.org/10.1007/s11069-015-2075-1

    Article  Google Scholar 

  34. Parise M, Jibson RW (2000) A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng Geol 58(3):251–270. https://doi.org/10.1016/S0013-7952(00)00038-7

    Article  Google Scholar 

  35. 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):1–32. https://doi.org/10.1007/s11069-012-0217-2

    Article  Google Scholar 

  36. Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45(10):1244–1256. https://doi.org/10.1016/j.asr.2010.01.006

    Article  Google Scholar 

  37. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51(2):350–365. https://doi.org/10.1016/j.cageo.2012.08.023

    Article  Google Scholar 

  38. Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the sera river basin using logistic regression model. Nat Hazards 85(3):1–24. https://doi.org/10.1007/s11069-016-2591-7

    Article  Google Scholar 

  39. Rampone S, Valente A (2012) Neural network aided evaluation of landslide susceptibility in southern Italy. Int J Mod Phys C 23(23):98–108. https://doi.org/10.1142/S0129183112500027

    Google Scholar 

  40. Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61(1):143–153. https://doi.org/10.1007/s11069-011-9846-0

    Article  Google Scholar 

  41. 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):1555–1576. https://doi.org/10.1007/s11069-014-1378-y

    Article  Google Scholar 

  42. Shit PK, Bhunia GS, Maiti R (2016) Potential landslide susceptibility mapping using weighted overlay model (WOM). Model Earth Syst Environ 2(1):21. https://doi.org/10.1007/s40808-016-0078-x

    Article  Google Scholar 

  43. Su F, Cui P, Zhang J, Xiang L (2010) Susceptibility assessment of landslides caused by the Wenchuan earthquake using a logistic regression model. J Mt Sci 7(3):234–245. https://doi.org/10.1007/s11629-010-2015-1

    Article  Google Scholar 

  44. Suzen ML, Doyuran V (2004a) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71(3–4):303–321. https://doi.org/10.1016/S0013-7952(03)00143-1

    Article  Google Scholar 

  45. Suzen ML, Doyuran V (2004b) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679. https://doi.org/10.1007/s00254-003-0917-8

    Article  Google Scholar 

  46. Tham LG (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582. https://doi.org/10.1016/j.geomorph.2008.02.011

    Article  Google Scholar 

  47. Trigila A, Iadanza C, Esposito C, Scarascia MG (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136. https://doi.org/10.1016/j.geomorph.2015.06.001

    Article  Google Scholar 

  48. Wang M, Liu M, Yang S, Shi P (2014) Incorporating triggering and environmental factors in the analysis of earthquake-induced landslide hazards. Int J Disaster Risk Sci 5(2):125–135. https://doi.org/10.1007/s13753-014-0020-7

    Article  Google Scholar 

  49. Wang Q, Wang D, Huang Y, Wang Z, Zhang L, Guo Q (2015) Landslide susceptibility mapping based on selected optimal combination of landslide predisposing factors in a large catchment. Sustainability 7(12):16653–16669. https://doi.org/10.3390/su71215839

    Article  Google Scholar 

  50. Wang F, Xu P, Wang C, Wang N, Jiang N (2017) Application of a GIS-based slope unit method for landslide susceptibility mapping along the Longzi River, southeastern Tibetan plateau, China. ISPRS Int J Geo-Inf 6(6):172. https://doi.org/10.3390/ijgi6060172

    Article  Google Scholar 

  51. Wei G, Feng XT (2004) Study on displacement predication of landslide based on grey system and evolutionary neural network. Rock Soil Mech 25(4):275–275

    Google Scholar 

  52. Xiao L (1995) Relative analysis between strong rainfall process and geological hazards Chongqing City. Chin J Geol Hazard Control 6:39–42

    Google Scholar 

  53. Xie M, Esaki T, Zhou G (2004) GIS-based probabilistic mapping of landslide hazard using a three-dimensional deterministic model. Nat Hazards 33(2):265–282. https://doi.org/10.1023/B:NHAZ.0000037036.01850.0d

    Article  Google Scholar 

  54. Xie N, Xin J, Liu S (2014) China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model. Nat Hazards 71(2):1067–1089. https://doi.org/10.1007/s11069-013-0662-6

    Article  Google Scholar 

  55. Xu W, Yu W, Jing S (2013) Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Nat Hazards 65(3):1379–1392. https://doi.org/10.1007/s11069-012-0414-z

  56. Yan TZ (1988) Recent advances of quantitative prognoses of landslide in China. In: Proceedings of the 5th international symposium on landslides, Lausanne, Switzerland, pp 10–15

    Google Scholar 

  57. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266. https://doi.org/10.1016/j.enggeo.2005.02.002

    Article  Google Scholar 

  58. Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. In: Proceedings of the 5th international symposium on landslides, Lausanne, Switzerland, pp 10–15

  59. Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6(8):2873–2888. https://doi.org/10.1007/s12517-012-0610-x

    Article  Google Scholar 

  60. Zhuang J, Peng J, Xu Y, Xu Q, Zhu X, Li W (2016) Assessment and mapping of slope stability based on slope units: a case study in Yan’an, China. J Earth Syst Sci 125(7):1–12. https://doi.org/10.1007/s12040-016-0741-7

    Article  Google Scholar 

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Acknowledgments

The research is supported by National Science and Technology Major Project of the Ministry of Science and Technology of China (project No. 2017YFB0503704) and National Nature Science Foundation of China (project No. 41671380) and is funded by the Foundation of Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geoinformation.

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Correspondence to Yumin Chen.

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Communicated by: H. A. Babaie

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Ba, Q., Chen, Y., Deng, S. et al. A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inform 11, 373–388 (2018). https://doi.org/10.1007/s12145-018-0335-9

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Keywords

  • Landslide
  • Susceptibility assessment
  • Slope unit
  • Grid cell
  • Information value model
  • Mapping unit