Robustness evaluation of fuzzy expert system and extreme learning machine for geographic information system-based landslide susceptibility zonation: A case study from Indian Himalaya

  • Bipin PeethambaranEmail author
  • R. Anbalagan
  • K. V. Shihabudheen
  • Ajanta Goswami
Original Article


In the last few decades, with the development of computers and geographic information system (GIS), a wide range of landslide susceptibility zonation (LSZ) techniques were orchestrated by various researchers around the globe. Among them, the artificial intelligence (AI) have been distinctly regarded as the most effective and suitable approach to part with GIS for LSZ. Though, suitability of AI for LSZ is well addressed in the landslide literature, noises of processing data, choice of causative factors and landslide density of study area are the number of hindrances that cause quandary over preference of ideal AI technique among many. The current study intends to analyse and compare the predictive performance of two entirely different AI techniques, fuzzy expert system (FES), a bivariate statistical technique, and extreme learning machine (ELM), a multivariate statistical technique for GIS based LSZ. The Mussoorie Township, a famous tourist destination in the Indian State of Uttarakhand was taken as the study area. Thematic layers of relevant causative factors and landslide inventory were prepared for the study area through field survey, remote sensing, and GIS. The resultant landslide susceptibility maps (LSM) of the study area, LSM-I of FES and LSM-II of ELM were critically evaluated and compared with the aid of landslide inventory of the study area.


Landslide susceptibility zonation Geographic information system Artificial intelligence Fuzzy expert system Extreme learning machine Mussoorie Township 



This paper forms a part of doctoral study of Mr. Bipin Peethambaran, which is financially supported by the University Grant Commission (UGC) of India. The authors would like to thank the editor-in-chief, Dr. Gunter Dörhöfer and anonymous reviewers for their constructive suggestions and guidance.


  1. Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:12665CrossRefGoogle Scholar
  2. Aghdam IN, Pradhan B, Panahi M (2017) Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environ Earth Sci 76:1–22CrossRefGoogle Scholar
  3. Ahmed R, Sajjad H (2018) Analyzing factors of groundwater potential and its relation with population in the Lower Barpani Watershed, Assam, India. Nat Resour Res 27:503–515CrossRefGoogle Scholar
  4. Akgun A, Sezer EA, Nefeslioglu HA et al (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34CrossRefGoogle Scholar
  5. An K, Kim S, Chae T, Park D (2018) Developing an accessible landslide susceptibility model using open-source resources. Sustainability 10:1–13CrossRefGoogle Scholar
  6. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  7. Anbalagan R (1996) Landslide hazard and risk assessment mapping of mountainous terrains—a case study from Kumaun Himalaya, India. Eng Geol 43:237–246CrossRefGoogle Scholar
  8. Anbalagan R, Singh B, Chakraborty D, Kohli A (2007) A field manual for landslide investigations. Department of Science Technology Government of India, New DelhiGoogle Scholar
  9. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (ganga) valley, himalayas. Int J Remote Sens 25:559–572CrossRefGoogle Scholar
  10. Barnard PL, Owen LA, Sharma MC, Finkela RC (2001) Natural and human-induced landsliding in the Garhwal Himalaya of Northern India. Geomorphology 40:21–35CrossRefGoogle Scholar
  11. Cao Y, Yin K, Alexander DE, Zhou C (2016) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13:725–736CrossRefGoogle Scholar
  12. Cascini L, Critelli S, Gulla G, Di Nocera S (1991) A methodological approach to landslide hazard assessment: a case history. In: Proceedings of the 16th international landslide conference, Balkema, Rotterdam. pp 899–904Google Scholar
  13. Chimidi G, Raghuvanshi TK, Suryabhagavan KV (2017) Landslide hazard evaluation and zonation in and around Gimbi town, western Ethiopia—a GIS-based statistical approach. Appl Geom 9:219–236CrossRefGoogle Scholar
  14. Choi J, Oh HJ, Lee HJ et al (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23CrossRefGoogle Scholar
  15. Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: The Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961CrossRefGoogle Scholar
  16. D’Ambrosio D, Di Gregorio S, Iovine G (2003) Simulating debris flows through a hexagonal cellular automata model: SCIDDICA S3–hex. Nat Hazard Earth Syst Sci 3:545–559CrossRefGoogle Scholar
  17. Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391CrossRefGoogle Scholar
  18. Den Hartog MH, Babuška R, Deketh HJR et al (1997) Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. Int J Approx Reason 16:43–66CrossRefGoogle Scholar
  19. Dhakal AS, Sidle RC (2003) Long-term modelling of landslides for different forest management practices. Earth Surf Process Landf 28:853–868CrossRefGoogle Scholar
  20. Erener A, Sebnem H, Düzgün B (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68CrossRefGoogle Scholar
  21. Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66:327–343CrossRefGoogle Scholar
  22. Freund JE (1992) Mathematical statistics, 5th edn. Printice-Hall of India Pvt. Ltd., New DelhiGoogle Scholar
  23. Gokceoglu C, 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 44:147–161CrossRefGoogle Scholar
  24. Gokceoglu C, Sonmez H, Nefeslioglu HA et al (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81:65–83CrossRefGoogle Scholar
  25. Grima MA, Babuška R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349CrossRefGoogle Scholar
  26. Gupta P, Anbalagan R (1997) Slope stability of Tehri Dam Reservoir Area, India, using landslide hazard zonation (LHZ) mapping. Q J Eng Geol Hydrogeol 30:27–36. CrossRefGoogle Scholar
  27. Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himal Eng Geol 28:119–131CrossRefGoogle Scholar
  28. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. CrossRefGoogle Scholar
  29. Huang F, Yin K, Huang J et al (2017) Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol 223:11–22CrossRefGoogle Scholar
  30. Humbert M (1977) Risk Mapping of areas exposed to Movements of Soil and Sub-soil: French “Zermos” maps. Bull Int Assoc Eng Geol 16:80–82CrossRefGoogle Scholar
  31. Iovine G, Di Gregorio S, Lupiano V (2003) Debris-flow susceptibility assessment through cellular automata modeling: an example from 15 to 16 December 1999 disaster at Cervinara and San Martino Valle Caudina (Campania, Southern Italy). Nat Hazard Earth Syst Sci 3:457–468CrossRefGoogle Scholar
  32. Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58:271–289CrossRefGoogle Scholar
  33. Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng ASCE 118:475–494CrossRefGoogle Scholar
  34. Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri Reservoir Rim Region, Uttarakhand. J Geol Soc India 87:271–286CrossRefGoogle Scholar
  35. Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping and prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295:115–125CrossRefGoogle Scholar
  36. Lee S (2007) Landslide susceptibility mapping using an artificial neural network in the Gangneung are, Korea. Int J Remote Sens 28:4763–4783CrossRefGoogle Scholar
  37. Lee S, Min K (2001) Statistical analyses of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  38. Lian C, Zeng Z, Yao W, Tang H (2014) Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput Appl 24:99–107CrossRefGoogle Scholar
  39. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13CrossRefGoogle Scholar
  40. Martha TR, Kumar KV (2013) September, 2012 landslide events in Okhimath, India-an assessment of landslide consequences using very high resolution satellite data. Landslides 10:469–479CrossRefGoogle Scholar
  41. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological geomorphological and biological applications. Hydrol Process 5:3–30CrossRefGoogle Scholar
  42. Nefeslioglu HA, Gokceoglu C, Sonmez H (2006) Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles. Eng Geol 85:251–269CrossRefGoogle Scholar
  43. Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36:325–334CrossRefGoogle Scholar
  44. Peethambaran B, Anbalagan R, Shihabudheen KV (2018) Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system—a comparative study. Nat Hazards. CrossRefGoogle Scholar
  45. Pham BT, Tien Bui D, Pourghasemi HR et al (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273CrossRefGoogle Scholar
  46. Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775CrossRefGoogle Scholar
  47. Polykretis C, Ferentinou M, Chalkias C (2014) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ 74:27–45CrossRefGoogle Scholar
  48. 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 63:329–349CrossRefGoogle Scholar
  49. Saha AK, Gupta RP, Sarkar I et al (2005) An approach for GIS-based statistical landslide susceptibility zonation-with a case study in the Himalayas. Landslides 2:61–69CrossRefGoogle Scholar
  50. Sahana M, Sajjad H (2017) Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag District, India. J Mt Sci 14:2150–2167CrossRefGoogle Scholar
  51. Sahana M, Dutta S, Sajjad H (2018a) Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. Int J Urban Sci 0:1–21Google Scholar
  52. Sahana M, Hong H, Sajjad H (2018b) Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Sci Total Environ 628–629:1557–1566CrossRefGoogle Scholar
  53. Sahana M, Hong H, Sajjad H et al (2018c) Assessing deforestation susceptibility to forest ecosystem in Rudraprayag District, India using fragmentation approach and frequency ratio model. Sci Total Environ 627:1264–1275CrossRefGoogle Scholar
  54. Sarkar S, Kanungo DP, Sharma S (2015) Landslide hazard assessment in the upper Alaknanda valley of Indian Himalayas. Geomat Nat Hazards Risk 6:308–325Google Scholar
  55. Sati SP, Naithani A, Rawat GS (1998) Landslides in the Garhwal Lesser Himalaya, UP, India. Environmentalist 18:149–155CrossRefGoogle Scholar
  56. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219CrossRefGoogle Scholar
  57. Shihabudheen KV, Peethambaran B (2017) Landslide displacement prediction technique using improved neuro-fuzzy system. Arab J Geosci. CrossRefGoogle Scholar
  58. Shihabudheen KV, Pillai GN, Peethambaran B (2017) Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS). Appl Soft Comput 61:892–904CrossRefGoogle Scholar
  59. Shou KJ, Wang CF (2003) Analysis of the Chiufengershan landslide triggered by the 1999 Chi–Chi earthquake in Taiwan. Eng Geol 68:237–250CrossRefGoogle Scholar
  60. Sonmez H, Gokceoglu C, Ulusay R (2003) An application of fuzzy sets to the Geological Strength Index (GSI) system used in rock engineering. Eng Appl Artif Intell 16:251–269CrossRefGoogle Scholar
  61. Tazik E, Jahantab Z, Bakhtiari M et al (2014) Landslide susceptibility mapping by combining the three methods fuzzy logic, frequency ratio and analytical hierarchy process in Dozain basin. XL:15–17. CrossRefGoogle Scholar
  62. Temesgen B, Mohammed MU, Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Phys Chem Earth Part C Solar Terr Planet Sci 26:665–675Google Scholar
  63. Tien Bui D, Tuan TA, Klempe H et al (2016) 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 13:361–378CrossRefGoogle Scholar
  64. Valdiya KS (1980) Geology of Kumaun Lesser Himalaya. Wadia Institute Himalayan Geology, DehradunGoogle Scholar
  65. Varnes DJ (1978) Slope movement types and processes. Transp Res Board Spec Rep 11–33. In: Special report 176: landslides: analysis and control, transportation research board, Washington, DCGoogle Scholar
  66. Vasu NN, Lee SR (2016) A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea. Geomorphology 263:50–70CrossRefGoogle Scholar
  67. Wright RH, Nilsen TH (1974) Isopleth map of landslide deposits, Southern San Francisco Bay Region, California. US Geological Survey Miscellaneous Field Studies Map, MF-550 (Scale 1:250,000)Google Scholar
  68. 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 35:1125–1138CrossRefGoogle Scholar
  69. Yilmaz C, Topal T, Süzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak–Turkey). Environ Earth Sci 65:2161–2178CrossRefGoogle Scholar
  70. Zhou G, Esaki T, Mitani Y et al (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386CrossRefGoogle Scholar
  71. Zhou C, Yin K, Cao Y et al (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the three Gorges Reservoir Area, China. Comput Geosci 112:23–37CrossRefGoogle Scholar
  72. Zhu AX, Wang R, Qiao J et al (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138CrossRefGoogle Scholar
  73. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Electrical EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations