Advertisement

Earth Science Informatics

, Volume 11, Issue 4, pp 605–622 | Cite as

Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

  • Haoyuan Hong
  • Aiding Kornejady
  • Adel Soltani
  • Seyed Vahid Razavi Termeh
  • Junzhi Liu
  • A-Xing ZhuEmail author
  • Arastoo Yari hesar
  • Baharin Bin Ahmad
  • Yi WangEmail author
Research Article
  • 474 Downloads

Abstract

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.

Keywords

Receiver operating characteristic Frequency ratio Evidential belief function Mahalanobis distance 

Notes

Acknowledgements

The authors would like to acknowledge the anonymous reviewers and the editor for their helpful comments on a previous version of the manuscript. Also, the authors wish to express their sincere thanks to Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84) for their financial supports in this research.

References

  1. Ahmed B (2014) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. LandslidesGoogle Scholar
  2. Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36Google Scholar
  3. Barker DM et al (2009) Longitudinal distributions of river flood power: the combined automated flood, elevation and stream power (CAFES) methodology. Earth Surf Process Landf 34:280–290Google Scholar
  4. Barlow J et al (2015) Seismically-induced mass movements and volumetric fluxes resulting from the 2010 Mw=7.2 earthquake in the Sierra Cucapah, Mexico. Geomorphology 230:138–145Google Scholar
  5. Bièvre G, Jongmans D, Goutaland D, Pathier E, Zumbo V (2015) Geophysical characterization of the lithological control on the kinematic pattern in a large clayey landslide (Avignonet, French Alps). LandslidesGoogle Scholar
  6. Bonham-Carter GF (1994) Geographic Information Systems for Geoscientists. Computer Methods in the Geosciences 4(4):1–2Google Scholar
  7. Bordoni M, Meisina C, Valentino R, Bittelli M, Chersich S (2015a) Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS. Nat Hazards Earth Syst Sci 15:1025–1050Google Scholar
  8. Bordoni M et al (2015b) Hydrological factors affecting rainfall-induced shallow landslides: From the field monitoring to a simplified slope stability analysis. Eng Geol 193:19–37Google Scholar
  9. Chen Z, Liang S, Ke Y, Yang Z, Zhao H (2017a) Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China. Geocarto International:1–20Google Scholar
  10. Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017b) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85Google Scholar
  11. Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu AX, Pei X, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135Google Scholar
  12. Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu AX (2018b) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149Google Scholar
  13. Ciampalini A et al (2015) Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase. In: GeomorphologyGoogle Scholar
  14. Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250Google Scholar
  15. Damm B, Klose M (2015) The landslide database for Germany: Closing the gap at national level. Geomorphology 249(15):82–93Google Scholar
  16. Das HO, Sonmez H, Gokceoglu C, Nefeslioglu HA (2012) Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley). Landslides 10:433–454Google Scholar
  17. Day S et al (2015) Submarine landslide deposits of the historical lateral collapse of Ritter Island, Papua New Guinea. Mar Pet Geol 67:419–438Google Scholar
  18. Demir G et al (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65(3):1481–1506Google Scholar
  19. Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat:325–339Google Scholar
  20. Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto International 32:619–639Google Scholar
  21. Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞHB, Akgün H (2014) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29:132–158Google Scholar
  22. Elmoulat M, Brahim LA, Mastere M, Jemmah AI (2015) Mapping of Mass Movements Susceptibility in the Zoumi Region Using Satellite Image and GIS Technology (Moroccan Rif). International Journal of Scientific & Engineering Research 6:210–217Google Scholar
  23. Fan X, Rossiter DG, van Westen CJ, Xu Q, Görüm T (2014) Empirical prediction of coseismic landslide dam formation. Earth Surf Process Landf 39:1913–1926Google Scholar
  24. Faraji Sabokbar H, Shadman Roodposhti M, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology 226:15–24Google Scholar
  25. Gallo F, Lavé J (2014) Evolution of a large landslide in the High Himalaya of central Nepal during the last half-century. Geomorphology 223:20–32Google Scholar
  26. Galve JP, Cevasco A, Brandolini P, Soldati M (2014) Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling. Landslides 12:101–114Google Scholar
  27. Ganapathy GP, Rajawat AS (2015) Use of hazard and vulnerability maps for landslide planning scenarios: a case study of the Nilgiris, India. Nat Hazards 77:305–316Google Scholar
  28. Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11Google Scholar
  29. Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Google Scholar
  30. Gutiérrez F et al (2015) Large landslides associated with a diapiric fold in Canelles Reservoir (Spanish Pyrenees): Detailed geological–geomorphological mapping, trenching and electrical resistivity imaging. Geomorphology 241:224–242Google Scholar
  31. Havenith HB et al (2015) Tien Shan Geohazards Database: Landslide susceptibility analysis. GeomorphologyGoogle Scholar
  32. Hong H, Pradhan B, Xu C, Tien Bui D (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281Google Scholar
  33. Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016a) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9(2):112Google Scholar
  34. Hong H, Pourghasemi HR, Pourtaghi ZS (2016b) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118Google Scholar
  35. Hong H, Liu J, Zhu AX, Shahabi H, Pham BT, Chen W, Pradhan B, Bui DT (2017a) A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). Environ Earth Sci 76(19):652Google Scholar
  36. Hong H, Chen W, Xu C,Youssef A M, Pradhan B, Dieu T B. (2017b) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto International 32(2):139–154Google Scholar
  37. Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. LandslidesGoogle Scholar
  38. Iovine G, Greco R, Gariano SL, Pellegrino AD, Terranova OG (2014) Shallow-landslide susceptibility in the Costa Viola mountain ridge (Southern Calabria, Italy) with considerations on the role of causal factors. Nat Hazards 73(1):111–136.  https://doi.org/10.1007/s11069-014-1129-0 CrossRefGoogle Scholar
  39. Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165Google Scholar
  40. Kirschbaum D, Stanley T, Zhou Y (2015) Spatial and temporal analysis of a global landslide catalog. GeomorphologyGoogle Scholar
  41. Kornejady A, Ownegh M, Bahremand A (2017a) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152:144–162Google Scholar
  42. Kornejady A, Ownegh M, Rahmati O, Bahremand A (2017b) Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND. Geocarto International:1–68Google Scholar
  43. Kritikos T, Robinson TR, Davies TRH (2015) Regional coseismic landslide hazard assessment without historical landslide inventories: A new approach. J Geophys Res Earth Surf 120:711–729Google Scholar
  44. Lacroix P, Berthier E, Maquerhua ET (2015) Earthquake-driven acceleration of slow-moving landslides in the Colca valley, Peru, detected from Pléiades images. Remote Sens Environ 165:148–158Google Scholar
  45. Larsen IJ, Montgomery DR (2012) Landslide erosion coupled to tectonics and river incision. Nat Geosci 5:468–473Google Scholar
  46. Le QH, Van Nguyen TH, Do MD, Le TCH, Nguyen HK, Luu TB (2018) TXT-tool 1.084–3.1: Landslide Susceptibility Mapping at a Regional Scale in Vietnam, In Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools (pp. 161–174). Springer, ChamGoogle Scholar
  47. Lian C, Zeng Z, Yao W, Tang H (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch Env Res Risk A 28:1957–1972Google Scholar
  48. Liu Z, Shao J, Xu W, Chen H, Shi C (2013) Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides 11:889–896Google Scholar
  49. Mahalanobis PC (1936) On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India 1936:49–55Google Scholar
  50. Mansouri Daneshvar MR (2014) Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides 11:1079–1091Google Scholar
  51. Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97Google Scholar
  52. Meten M, Bhandary NP, Yatabe R (2015) Application of GIS-based fuzzy logic and rock engineering system (RES) approaches for landslide susceptibility mapping in Selelkula area of the Lower Jema River Gorge, Central Ethiopia. Environ Earth Sci 74:3395–3416Google Scholar
  53. Mogaji K, Omosuyi G, Adelusi A, Lim H (2016) Application of GIS-Based Evidential Belief Function Model to Regional Groundwater Recharge Potential Zones Mapping in Hardrock Geologic Terrain. Environmental Processes 3:93–123Google Scholar
  54. 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 61:221–236Google Scholar
  55. Moore ID, Grayson RB (1991) Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resour Res 27(6):1177–1191Google Scholar
  56. Park N-W (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62(2):367–376Google Scholar
  57. Peng L et al (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 204:287–301Google Scholar
  58. Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps - case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118.  https://doi.org/10.5194/nhess-14-95-2014 CrossRefGoogle Scholar
  59. Poiraud A (2014) Landslide susceptibility–certainty mapping by a multi-method approach: A case study in the Tertiary basin of Puy-en-Velay (Massif central, France). Geomorphology 216:208–224Google Scholar
  60. Pourghasemi HR (2016) GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scand J For Res 31:80–98Google Scholar
  61. Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75:1–17Google Scholar
  62. Pourghasemi H, Moradi H, Aghda SF (2013a) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779Google Scholar
  63. Pourghasemi HR et al (2013b) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122(2):349–369Google Scholar
  64. Pourghasemi HR, Moradi HR, Aghda SF, Gokceoglu C, Pradhan B (2014) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7(5):1857–1878Google Scholar
  65. Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775Google Scholar
  66. 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 51:350–365Google Scholar
  67. Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042Google Scholar
  68. Rahmati O, Kornejady A, Samadi M, Nobre AD, Melesse AM (2018) Development of an automated GIS tool for reproducing the HAND terrain model. Environ Model Softw 102:1–12Google Scholar
  69. Ramos-Cañón A, Prada-Sarmiento L, Trujillo-Vela M, Macías J, Santos-R A (2015) Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá. Colombia Landslides 13(4):671–681Google Scholar
  70. Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sens 6:9600–9652Google Scholar
  71. Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T (2016) Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method. Entropy 18(10):343Google Scholar
  72. Shafer G (1976) A mathematical theory of evidence. Technometrics 20:242Google Scholar
  73. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5:9899Google Scholar
  74. Sharma LP, Patel N, Ghose MK, Debnath P (2014) Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India. Nat Hazards 75:1555–1576Google Scholar
  75. Shi X, Zhang L, Balz T, Liao M (2015) Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data. ISPRS J Photogramm Remote Sens 105:128–140Google Scholar
  76. Su C, Wang L, Wang X, Huang Z, Zhang X (2015) Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards 76:1759–1779Google Scholar
  77. Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71(3):303–321Google Scholar
  78. Tahmassebip Swets JA (1988) Measuring the accuracy of diagnostic systems. science, 240(4857), 1285-1293. oor, N., Rahmati, O., Noormohamadi, F., Lee, S., 2016. Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9:1–18Google Scholar
  79. Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451Google Scholar
  80. Tien Bui D et al (2015a) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics. Natural Hazards and Risk 6:243–271Google Scholar
  81. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015b) 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. LandslidesGoogle Scholar
  82. Tomás R, Li Z, Lopez-Sanchez JM, Liu P, Singleton A (2015) Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide. LandslidesGoogle Scholar
  83. Topal T, Hatipoglu O (2015) Assessment of slope stability and monitoring of a landslide in the Koyulhisar settlement area (Sivas, Turkey). Environmental Earth SciencesGoogle Scholar
  84. Trigila, A., Iadanza, C., Esposito, C., Scarascia-Mugnozza, G., 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). GeomorphologyGoogle Scholar
  85. Tsangaratos P, Ilia I (2015) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. LandslidesGoogle Scholar
  86. Tsangaratos P, Ilia I, Hong H, Chen W, Xu C (2016) Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng county, China. Landslides, 1–21.  https://doi.org/10.1007/s10346-016-0769-4 Google Scholar
  87. Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124–135Google Scholar
  88. Wang G et al (2015) A methodology to derive precise landslide displacement time series from continuous GPS observations in tectonically active and cold regions: a case study in Alaska. Nat Hazards 77:1939–1961Google Scholar
  89. Wood JL, Harrison S, Reinhardt L (2015) Landslide inventories for climate impacts research in the European Alps. Geomorphology 228:398–408Google Scholar
  90. Xing AG et al (2014) Dynamic analysis and field investigation of a fluidized landslide in Guanling, Guizhou, China. Eng Geol 181:1–14Google Scholar
  91. Xu C, Xu X, Shyu JBH, Zheng W, Min W (2014) Landslides triggered by the 22 July 2013 Minxian–Zhangxian, China, Mw 5.9 earthquake: Inventory compiling and spatial distribution analysis. J Asian Earth Sci 92:125–142Google Scholar
  92. 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 85:274–287Google Scholar
  93. Yao W, Zeng Z, Lian C, Tang H (2015) Training enhanced reservoir computing predictor for landslide displacement. Eng Geol 188:101–109Google Scholar
  94. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey: University of Melbourne, Department, 200Google Scholar
  95. Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth area, Jizan, Kingdom of Saudi Arabia, using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci 73:8499–8518Google Scholar
  96. Youssef AM, Pourghasemi HR, El-Haddad BA, Dhahry BK (2015) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia. Bull Eng Geol Environ.  https://doi.org/10.1007/s10064-015-0734-9 Google Scholar
  97. Yusof NM, Pradhan B, Shafri HZM, Jebur MN, Yusoff Z (2015) Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data. Arab J GeosciGoogle Scholar
  98. Zeybek M, Şanlıoğlu İ, Özdemir A (2015) Monitoring landslides with geophysical and geodetic observations. Environ Earth SciGoogle Scholar
  99. Zhang Z et al (2016) GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models. Environ Earth Sci 75(11):948Google Scholar
  100. Zhao H, Yao L, Mei G, Liu T, Ning Y (2017) A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map. Entropy 19(8):396Google Scholar
  101. Zhou S, Fang L, Liu B (2015) Slope unit-based distribution analysis of landslides triggered by the April 20, 2013, Ms 7.0 Lushan earthquake. Arab J GeosciGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of EducationNanjingChina
  2. 2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)NanjingChina
  3. 3.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and ApplicationNanjingChina
  4. 4.Young Researchers and Elite Club, Gorgan BranchIslamic Azad UniversityGorganIran
  5. 5.Faculty member of the Department of Agricultural Technology & EngineeringPayam Noor UniversityTehranIran
  6. 6.Faculty of Geodesy & Geomatics EngineeringK. N. Toosi University of TechnologyTehranIran
  7. 7.Department of GeographyUniversity of Mohaghegh ArdabiliArdabilIran
  8. 8.Department of Geoinformation, Faculty of Geoinformation and Real EstateUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  9. 9.Institute of Geophysics and GeomaticsChina University of GeosciencesWuhanChina

Personalised recommendations