Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model

  • Qadir Ashournejad
  • Ali HosseiniEmail author
  • Biswajeet PradhanEmail author
  • Seyed Javad Hosseini
Original Paper


The popular use of geographical information system (GIS) technology in landslide assessment ensures good, accurate and fast results with less cost and manages mass movements and identifies safe areas for the development of new settlements. In the literature, different methods have been applied in landslide susceptibility assessment using various models. In these studies, the methods are either expert knowledge-based or data-driven approaches. These two approaches produce different outcomes based on their specific characteristics. Thus, the simultaneous use of a set of methods using fusion techniques at the decision-level improves the results. This study aims to investigate and identify a landslide zonation model by combining different methods in GIS and integrate the results (incorporated in the decision level) to achieve optimum accuracy. In this study, landslide hazard mapping was performed using knowledge-based models such as simple additive weighting (SAW) and fuzzy gamma operator and data-driven models namely radial basis function link network (RBFLN) and probabilistic neural network (PNN). Evaluation results indicate that data-driven methods have better performance than knowledge-based methods based on expert opinion. The SAW method also has a better result than other knowledge-based methods, such as fuzzy gamma. The assessment results of the randomly selected landslide control points indicate that PNN, SAW, RBFLN and fuzzy gamma have accuracies of 82.3, 69.62, 65.39 and 61.26% in landslide zoning, respectively. Meanwhile, weighted mean, maximum, median and minimum were used to incorporate the results in decision-level fusion; results show improved accuracies of 70.53, 83.5, 67.39 and 60.27% for each criterion, respectively.


Landslide susceptibility Hazard zoning Data-driven models Knowledge-based models Geographic information system (GIS) 


Author contributions

The manuscript is the final outcome of a close teamwork: individual contributions are not distinguishable. All the authors read and approved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.


  1. Aghda SF, Bagheri V (2015) Evaluation of earthquake-induced landslides hazard zonation methods: a case study of Sarein, Iran, earthquake (1997). Arab J Geosci 8(9):7207–7227CrossRefGoogle Scholar
  2. Akgün A, Türk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region, NW Turkey. Computers & geosciences 37(9):1515–1524Google Scholar
  3. Alkhasawneh MS, Ngah UK, Tay LT, Isa M, Ashidi N, Al-Batah MS (2014) Modeling and testing landslide hazard using decision tree. J Appl Math 2014:1–9CrossRefGoogle Scholar
  4. Alvioli M, Marchesini I, Reichenbach P, Rossi M, Ardizzone F, Fiorucci F, Guzzetti F (2016) Automatic delineation of geomorphological slope units with r. slopeunits v1. 0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9(11):3975–3991CrossRefGoogle Scholar
  5. Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  6. Atkinson PM, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the central Apennines, Italy. Geomorphology 130(1–2):55–64CrossRefGoogle Scholar
  7. Avalon Cullen C, Al-Suhili R, Khanbilvardi R (2016) Guidance index for shallow landslide hazard analysis. Remote Sens 8(10):866CrossRefGoogle Scholar
  8. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81CrossRefGoogle Scholar
  9. Ayenew T, Barbieri G (2005) Inventory of landslides and susceptibility mapping in the Dessie area, northern Ethiopia. Eng Geol 77(1–2):1–15CrossRefGoogle Scholar
  10. Bai SB, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN (2009) GIS-based and data-driven bivariate landslide-susceptibility mapping in the Three Gorges area, China. Pedosphere 19(1):14–20CrossRefGoogle Scholar
  11. Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(1–2):23–31CrossRefGoogle Scholar
  12. Basnet BB, Apan AA, Raine SR (2001) Selecting suitable sites for animal waste application using a raster GIS. Environ Manag 28(4):519–531CrossRefGoogle Scholar
  13. Behnia P (2007) Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran. Nat Resour Res 16(2):147–155CrossRefGoogle Scholar
  14. Bibalani GH, Majnonian B, Adeli E, Sanii H (2006) Slope stabilization with Gleditshia caspica and Parrotia persica. Int J Environ Sci Technol 2(4):381–385CrossRefGoogle Scholar
  15. Bui DT, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75(14):1101CrossRefGoogle Scholar
  16. Bui DT, Nguyen QP, Hoang ND, Klempe H (2017) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14(1):1–17CrossRefGoogle Scholar
  17. Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4(3):176–190CrossRefGoogle Scholar
  18. Chen Z, Wang J (2007) Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Nat Hazards 42(1):75–89CrossRefGoogle Scholar
  19. Chen W, Panahi M, Pourghasemi HR (2017a) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena 157:310–324CrossRefGoogle Scholar
  20. Chen W, Pourghasemi HR, Zhao Z (2017b) A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International 32(4):367–385CrossRefGoogle Scholar
  21. 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 626:1121–1135CrossRefGoogle Scholar
  22. Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Li S, Zhu AX (2018b) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149CrossRefGoogle Scholar
  23. 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 113:236–250CrossRefGoogle Scholar
  24. 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):397–406Google Scholar
  25. Corominas J, & Mavrouli J (2011). Living with landslide risk in Europe: assessment, effects of global change, and risk management strategies. Guidelines for landslide susceptibility, hazard and risk zoning. Documento técnico, SafeLand. 7th Framework Programme Cooperation Theme 6 Environment (including climate change) Sub-Activity 6.1.3 Natural HazardsGoogle Scholar
  26. Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology 111(1–2):79–87CrossRefGoogle Scholar
  27. Couture R (2011) Landslide terminology—national technical guidelines and best practices on landslides. Geological Survey of Canada, p. 12Google Scholar
  28. Daneshvar MRM, Bagherzadeh A (2011) Landslide hazard zonation assessment using GIS analysis at Golmakan watershed, northeast of Iran. Front Earth Sci 5(1):70–81CrossRefGoogle Scholar
  29. Erener A, Mutlu A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55CrossRefGoogle Scholar
  30. Fabbri AG, Chung CJF, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with a GIS? Nat Hazards 30(3):487–503CrossRefGoogle Scholar
  31. Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221CrossRefGoogle Scholar
  32. 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):85–98CrossRefGoogle Scholar
  33. Fernández DS, Lutz MA (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111(1–4):90–98CrossRefGoogle Scholar
  34. Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas. Eng Geol 28(1–2):119–131CrossRefGoogle Scholar
  35. Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184Google Scholar
  36. Hamza T, Raghuvanshi TK (2017) GIS based landslide hazard evaluation and zonation—a case from Jeldu District, Central Ethiopia. J King Saud Univ-Sci 29(2):151–165CrossRefGoogle Scholar
  37. Hong Y, Adler R, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43(2):245–256CrossRefGoogle Scholar
  38. Huabin W, Gangjun L, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567CrossRefGoogle Scholar
  39. 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
  40. 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
  41. Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408CrossRefGoogle Scholar
  42. Kıncal C, Akgun A, Koca MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59(4):745–756CrossRefGoogle Scholar
  43. Kuncheva LI, Bezdek JC, Duin RP (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn 34(2):299–314CrossRefGoogle Scholar
  44. Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Eng Geol 76(1–2):109–128CrossRefGoogle Scholar
  45. Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52(4):615–623CrossRefGoogle Scholar
  46. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113CrossRefGoogle Scholar
  47. Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3–4):289–302CrossRefGoogle Scholar
  48. Leite EP, de Souza Filho CR (2009) Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Comput Geosci 35(3):675–687CrossRefGoogle Scholar
  49. Lepore C, Kamal SA, Shanahan P, Bras RL (2012) Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environ Earth Sci 66(6):1667–1681CrossRefGoogle Scholar
  50. Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture: comparison of regression tree and Random Forest models. Geoderma 170:70–79CrossRefGoogle Scholar
  51. Looney CG (2002) Radial basis functional link nets and fuzzy reasoning. Neurocomputing 48(1–4):489–509CrossRefGoogle Scholar
  52. Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge-and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hard-rock terrain of Ahar catchment, Rajasthan, India. Environ Earth Sci 73(4):1871–1892CrossRefGoogle Scholar
  53. Maharaj RJ (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica, West Indies. Eng Geol 34(1–2):53–79CrossRefGoogle Scholar
  54. Marinoni O (2004) Implementation of the analytical hierarchy process with VBA in ArcGIS. Comput Geosci 30(6):637–646CrossRefGoogle Scholar
  55. Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4):379–400CrossRefGoogle Scholar
  56. Nazmfar H, Behesti A (2016) Application of combined model analytical network process and fuzzy logic models in landslide susceptibility zonation (case study: Chellichay catchment). Geogr Environ Plan 27(1):53–68Google Scholar
  57. Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng 2010:1–15CrossRefGoogle Scholar
  58. Neuhäuser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model. Landslides 9(4):511–528CrossRefGoogle Scholar
  59. Nykänen V (2008) Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Nat Resour Res 17(1):29–48CrossRefGoogle Scholar
  60. Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30CrossRefGoogle Scholar
  61. 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 64:180–197CrossRefGoogle Scholar
  62. Park I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. Int J Remote Sens 35(16):6089–6112CrossRefGoogle Scholar
  63. Perotto-Baldiviezo HL, Thurow TL, Smith CT, Fisher RF, Wu XB (2004) GIS-based spatial analysis and modeling for landslide hazard assessment in steeplands, southern Honduras. Agric Ecosyst Environ 103(1):165–176CrossRefGoogle Scholar
  64. Pham BT, Prakash I, Bui DT (2018) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303:256–270CrossRefGoogle Scholar
  65. Pourghasemi HR, Pradhan B, & Gokceoglu C (2012). Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. In Applied Mechanics and Materials. Trans Tech Publications (Vol. 225, pp. 486–491)Google Scholar
  66. Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349CrossRefGoogle Scholar
  67. Radiarta IN, Saitoh SI, Miyazono A (2008) GIS-based multi-criteria evaluation models for identifying suitable sites for Japanese scallop (Mizuhopecten yessoensis) aquaculture in Funka Bay, southwestern Hokkaido, Japan. Aquaculture 284(1–4):127–135CrossRefGoogle Scholar
  68. 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):1323–1346CrossRefGoogle Scholar
  69. Reichenbach P, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manag 54(6):1372–1384CrossRefGoogle Scholar
  70. Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91CrossRefGoogle Scholar
  71. Roodposhti MS, Rahimi S, Beglou MJ (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95CrossRefGoogle Scholar
  72. Ruff M, Czurda K (2008) Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology 94(3):314–324CrossRefGoogle Scholar
  73. Saaty TL (2003) Decision-making with the AHP: why is the principal eigenvector necessary. Eur J Oper Res 145(1):85–91CrossRefGoogle Scholar
  74. Saaty TL, & Vargas LG (1991). Prediction, projection, and forecasting: applications of the analytic hierarchy process in economics, finance, politics, games, and sports. Kluwer AcademicGoogle Scholar
  75. Sabokbar HF, Roodposhti MS, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology 226:15–24CrossRefGoogle Scholar
  76. Sadeghi B, Khalajmasoumi M (2015) A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renew Sust Energ Rev 43:818–831CrossRefGoogle Scholar
  77. Samodra G, Chen G, Sartohadi J, Kasama K (2017) Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java. Environ Earth Sci 76(4):184CrossRefGoogle Scholar
  78. Schuster RL, 1996. Socioeconomic significance of landslides. In: Turner AK, Schuster RL (Eds.), Landslides: investigation and mitigation, Chapter 2, special report 247. Transportation Research Board, National Research Council. National Academy Press, Washington, DC, pp. 12–35Google Scholar
  79. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899CrossRefGoogle Scholar
  80. Smith K (2013). Environmental hazards: assessing risk and reducing disaster. RoutledgeGoogle Scholar
  81. Srivastava V, Srivastava HB, Lakhera RC (2010) Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India. Geomat Nat Haz Risk 1(3):225–242CrossRefGoogle Scholar
  82. Stavrakoudis DG, Dragozi E, Gitas IZ, Karydas CG (2014) Decision fusion based on hyperspectral and multispectral satellite imagery for accurate forest species mapping. Remote Sensing 6(8):6897–6928Google Scholar
  83. 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(3–4):303–321CrossRefGoogle Scholar
  84. Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51(3):439–450CrossRefGoogle Scholar
  85. Thanh LN, De Smedt F (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district, Thua Thien Hue Province, Vietnam. Environ Earth Sci 66(7):1739–1752CrossRefGoogle Scholar
  86. Tissari S, Nykänen V, Lerssi J, Kolehmainen M (2007) Classification of soil groups using weights-of-evidence-method and RBFLN-neural nets. Nat Resour Res 16(2):159–169CrossRefGoogle Scholar
  87. Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114CrossRefGoogle Scholar
  88. Van Den Eeckhaut M, Marre A, Poesen J (2010) Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France). Geomorphology 115(1–2):141–155CrossRefGoogle Scholar
  89. Van Westen CJ, Van Asch TW, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65(2):167–184CrossRefGoogle Scholar
  90. Wang G, Zhang S, Yan C, Song Y, Sun Y, Li D, Xu F (2011) Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China. Comput Geosci 37(12):1976–1988CrossRefGoogle Scholar
  91. Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87(4):352–364CrossRefGoogle Scholar
  92. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72(1):1–12CrossRefGoogle Scholar
  93. 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):251–266CrossRefGoogle Scholar
  94. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138CrossRefGoogle Scholar
  95. Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides 3(2):149–158CrossRefGoogle Scholar
  96. Yu X, Wang Y, Niu R, Hu Y (2016) A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges Area, China. Int J Environ Res Public Health 13(5):487CrossRefGoogle Scholar
  97. Zêzere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267CrossRefGoogle Scholar
  98. Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76(11):405CrossRefGoogle Scholar
  99. 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):396CrossRefGoogle Scholar
  100. Zhu AX, Wang RX, Qiao JP, Qin CZ, Chen YB, Liu J, Du F, Yang L, Zhu TX (2014) An expert knowledge-based model to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138CrossRefGoogle Scholar
  101. Zhu AX, Miao Y, Wang R, Zhu T, Deng Y, Liu J, Yang L, Qin CZ, Hong H (2018) A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. Catena 166:317–327CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Remote Sensing and GISUniversity of TehranTehranIran
  2. 2.Department of Geography and Urban PlanningUniversity of TehranTehranIran
  3. 3.The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  4. 4.Department of Physical GeographyUniversity of TehranTehranIran

Personalised recommendations