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Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal

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Abstract

Nepal was affected by a catastrophic earthquake with Mw 7.8 on 25th April, 2015 with its epicenter in the central part of Barpak village. A number of co-seismic landslides were triggered by the main shock of the event and associated aftershocks. Due to the rugged topography and vicinity of the main shock, the village was extremely affected by co-seismic landslides. In total, 59 landslides were identified using Google Earth and were verified during the field survey in Barpak village. Furthermore, 11 conditioning factors, including Peak ground acceleration (PGA), epicenter proximity, fault proximity, geology, slope, elevation, plan curvature, profile curvature, topographic wetness index, drainage proximity and the sediment transport index were selected as independent variables for analysis. In this study, logistic regression (LR) and analysis of covariance (ANCOVA) models were used and their performance was assessed. Finally, the landslide susceptibility classes were produced and an evaluation of models was done by using receiver operating characteristic curves. The area under the curve for LR and ANCOVA showed 85.38 and 78.4% accuracy, respectively. Based on the overall assessments, the LR model was more accurate than the ANCOVA model for co-seismic landslide prediction in the study area. The result of this study can be used to mitigate landslide-induced hazards and for land-use planning.

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References

  • Beven K J and Kirkby M J 1979 A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable del’hydrologie du bassin versant; Hydrol. Sci. J. 24(1) 43–69.

    Article  Google Scholar 

  • Bednarik M, Magulová B, Matys M and Marschalko M 2010 Landslide susceptibility assessment of the Kra_ovany–Liptovský Mikuláš railway case study; Phys. Chem. Earth Parts A/B/C 35(3) 162–171.

    Article  Google Scholar 

  • Bray J D, Rathje E M, Augello A J and Merry S M 1998 Simplified seismic design procedure for lined solid-waste landfills; Geosynthet. Int. 5(1–2) 203–235.

    Article  Google Scholar 

  • Chung C J F and Fabbri A G 1999 Probabilistic prediction models for landslide hazard mapping; Photogramm. Eng. Rem. Sens. 65(12) 1389–1399.

    Google Scholar 

  • Dai F C, Lee C F, Li J and Xu Z W 2001 Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong; Environ. Geol. 40(3) 381–391.

    Article  Google Scholar 

  • Delgado J, Garrido J, López–Casado C, Martino S and Peláez J A 2011 On far field occurrence of seismically induced landslides; Eng. Geol. 123(3) 204–213.

    Article  Google Scholar 

  • Devkota K C, Regmi A D, Pourghasemi H R, Yoshida K, Pradhan B, Ryu I C, Dhital M R and Althuwaynee O F 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—165.

    Article  Google Scholar 

  • Dewey J F and Burke K C 1973 Tibetan, Variscan and Precambrian basement reactivation: Products of continental collision; J. Geol. 81(6) 683–692.

    Article  Google Scholar 

  • Dou J, Bui D T, Yunus A P, Jia K, Song X, Revhaug I, Xia H and Zhu Z 2015 Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan; PLoS One 10:e0133262, https://doi.org/10.1371/journal.pone.0133262.

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C and Van Asch T W 2004 Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques; Nat. Hazards 32(1) 1–23.

    Article  Google Scholar 

  • George D and Mallery P 2000 SPSS for Windows: A simple guide and reference; Allyn & Bacon, Boston, MA.

    Google Scholar 

  • Gökceoglu C and Aksoy H 1996 Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques; Eng. Geol. 44(1–4) 147–161.

    Article  Google Scholar 

  • Hanley J A and McNeil B J 1982 The meaning and use of the area under a receiver operating characteristic (ROC) curve; Radiology 143(1) 29–36.

    Article  Google Scholar 

  • Harp E L, Jibson RW, Kayen R E, Keefer D K, Sherrod B L, Carver G A, Collins B D, Moss R E S and Sitar N 2003 Landslides and liquefaction triggered by the M 7.9 Denali fault earthquake of 3 November 2002; GSA Today 13(8) 4–10.

    Article  Google Scholar 

  • Hosmer D W and Lemeshow S 2000 Interpretation of the fitted logistic regression model. Appl. Logistic Regression; 2nd edn, pp. 47–90.

  • Jade S and Sarkar S 1993 Statistical model for slope instability classifications; Eng. Geol. 36 71–98.

    Article  Google Scholar 

  • Jenks G 1967 The data model concept in statistical mapping; Int. Yearb. Cart. 7 347–356.

    Google Scholar 

  • Jibson R W, Harp E L and Michael J A 2000 A method for producing digital probabilistic seismic landslide hazard maps; Eng. Geol. 58(3) 271–289.

    Article  Google Scholar 

  • Kamp U, Growley B J, Khattak G A and Owen L A 2008 GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region; Geomorphology 101(4) 631–642.

    Article  Google Scholar 

  • Kargel J S, Leonard J, Shugar D H, Haritashya U K, Bevington A, Fielding E J, Fujita K, Geertsema M, Miles E S, Steiner J and Anderson E 2016 Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake; Science 351(6269) 1–18, 17, https://doi.org/10.1126/science.aac8353.

  • Kavzoglu T, Sahin E K and Colkesen I 2014 Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression; Landslides 11(3) 425–439.

    Article  Google Scholar 

  • Keefer D K 1994 The importance of earthquake-induced landslides to long-term slope erosion and slope-failure hazards in seismically active regions; Geomorphology 10(1–4) 265–284.

    Article  Google Scholar 

  • Kritikos T, Robinson T R and Davies T R 2015 Regional coseismic landslide hazard assessment without historical landslide inventories: A new approach; J. Geophys. Res. Earth Surface 120(4) 711–729.

    Article  Google Scholar 

  • Lee C T, Huang C C, Lee J F, Pan K L, Lin M L and Dong J J 2008 Statistical approach to earthquake-induced landslide susceptibility; Eng. Geol. 100(1) 43–58.

    Article  Google Scholar 

  • Lee S and Evangelista D G 2006 Earthquake-induced landslide-susceptibility mapping using an artificial neural network; Nat. Hazard Earth Syst. 6(5) 687–695.

    Article  Google Scholar 

  • Leech N L, Barrett K C and Morgan G A 2005 SPSS for intermediate statistics: Use and interpretation; Psychology Press.

  • Liao H W and Lee C T 2000 Landslides triggered by the Chi-Chi earthquake; In: Proceedings of the \(21{st}\) Asian Conference on Remote Sensing, Taipei, pp. 1–2.

  • Magliulo P, Di L A, Russo F and Zelano A 2008 Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: A case study in southern Italy; Nat. Hazards 47(3) 411–435.

    Article  Google Scholar 

  • Mahalingam R, Olsen M J and O’Banion M S 2016 Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study); Geomat. Nat. Hazard Risk 7(6) 1884–1907.

    Article  Google Scholar 

  • Mertler C A and Reinhart R V 2016 Advanced and multivariate statistical methods: Practical application and interpretation; Routledge.

  • Miles S B and Keefer D K 2000 Evaluation of seismic slope–performance models using a regional case study; Environ. Eng. Geosci. 6(1) 25–39.

    Article  Google Scholar 

  • Miles S B and Keefer D K 2007 Comprehensive areal model of earthquake-induced landslides: Technical specification and user guide; US Geol. Survey.

  • Miles S B and Keefer D K 2009a Evaluation of CAMEL – comprehensive areal model of earthquake-induced landslides; Eng. Geol. 104(1) 1–15.

    Article  Google Scholar 

  • Miles S B and Keefer D K 2009b Toward a comprehensive areal model of earthquake-induced landslides; Nat. Hazards Rev. 10(1) 19–28.

    Article  Google Scholar 

  • Moore I D and Burch G J 1986 Physical basis of the length-slope factor in the universal soil loss equation; Soil Sci. Soc. Am J. 50(5) 1294–1298.

    Article  Google Scholar 

  • Moore I D, Grayson R B and Ladson A R 1991 Digital terrain modelling: A review of hydrological, geomorphological, and biological applications; Hydrol. Process. 5 (3–30).

  • Newmark 1965 Effects of earthquakes on dams and embankments; Geotechnique 15(1965) 139–159.

    Article  Google Scholar 

  • O’brien R M 2007 A caution regarding rules of thumb for variance inflation factors; Quality & Quantity 41(5) 673–690.

    Article  Google Scholar 

  • Pradhan A M S and Kim Y T 2015 Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events; Environ. Earth Sci. 73(9) 5761–5771.

    Article  Google Scholar 

  • Pradhan A M S, Kang H S, Lee S and Kim Y T 2016 Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea; Geocarto. Int. 32(4) 420–441.

    Article  Google Scholar 

  • Pradhan A M S and Kim Y T 2016 Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping; Catena 140 125–139.

    Article  Google Scholar 

  • Pradhan A M S, Kang H S Lee S and Kim Y T 2017 Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea; Geocarto Int. 32(4) 420–441.

    Article  Google Scholar 

  • Pradhan B and Lee S 2010 Landslide susceptibility assessment and factor effect analysis: Back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling; Environ. Modell. Softw. 25 747–759.

    Article  Google Scholar 

  • Regmi A D, Dhital M R, Zhang J Q, Su L J and Chen X Q 2016 Landslide susceptibility assessment of the region affected by the 25 April 2015 Gorkha earthquake of Nepal; J. Mt. Sci. 13(11) 1941–1957.

    Article  Google Scholar 

  • Regmi N R, Giardino J R and Vitek J D 2010 Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA; Geomorphology 115 172–187.

    Article  Google Scholar 

  • Reneau S L and Dietrich W E 1987 Size and location of colluvial landslides in a steep forested landscape; IAHS–AISH Publ. 165 39–48.

    Google Scholar 

  • Saputra A, Gomez C, Hadmoko D S and Sartohadi J 2016 Coseismic landslide susceptibility assessment using geographic information system; Geoenviron. Disast. 3(1) 27.

    Article  Google Scholar 

  • Sarma S K 1975 Seismic stability of earth dams and embankments; Geotechnique 25(4) 743–761.

    Article  Google Scholar 

  • Seed H B, Lee K L, Idriss I M and Makdisi R 1973 Analysis of the slides in the San Fernando dams during the earthquake of Feb. 9, 1971; Earth Eng. Research Center, University of California, Berkeley; Report No. EERC 73–2, 150p.

  • Sharma C K 1990 Geology of Nepal Himalaya and Adjacent Countries; Sangeeta Sharma, Kathmandu.

  • Swets J A 1988 Measuring the accuracy of diagnostic systems; Science 240(4857) 1285.

    Article  Google Scholar 

  • Wang L J, Guo M, Sawada K, Lin J and Zhang J 2015 Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models; Catena 135 271–282.

    Article  Google Scholar 

  • Wasowski J, Keefer D K and Lee C T 2011 Towards the next generation of research on earthquake-induced landslides: current issues and future challenges; Eng. Geol. 122(1) 1–8.

    Article  Google Scholar 

  • Weng M C, Wu M H, Ning S K and Jou Y W 2011 Evaluating triggering and causative factors of landslides in Lawnon River Basin, Taiwan;, Taiwan; Eng. Geol. 123(1) 72–82.

    Article  Google Scholar 

  • Yegian M K, Marciano E A and Ghahraman V G 1991 Earthquake-induced permanent deformations: probabilistic approach; J. Geotech. Eng. 117(1) 35–50.

    Article  Google Scholar 

  • Zhou S, Chen G and Fang L 2016 Distribution pattern of landslides triggered by the 2014 Ludian earthquake of China: Implications for regional threshold topography and the seismogenic fault identification; ISPRS Int. J. Geo-Inf. 5(4) 46.

    Article  Google Scholar 

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Acknowledgements

This work was supported by a Research Grant of Pukyong National University (in the year 2016).

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Correspondence to Jung Chang Choi.

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Corresponding editor: Navin Juyal

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Shrestha, S., Kang, TS. & Choi, J.C. Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal. J Earth Syst Sci 127, 38 (2018). https://doi.org/10.1007/s12040-018-0936-1

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  • DOI: https://doi.org/10.1007/s12040-018-0936-1

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