Abstract
This paper mainly presents a case study of landslide vulnerability zonation along Tawaghat-Mangti route corridor in Kumaon Himalaya, India. An attempt is made to predict landslide susceptibility using back-propagation neural network (BPNN) and propose a suitable model for that zone, which can be successfully implemented for the prevention of slides. Various landslide affecting parameters such as lithology, slope, aspect, structure, geotechnical properties, land use, landslide inventory, and distance from recorded epicenter are used to model the landslide susceptibility. The database on the above parameters derived from satellite imageries, topographic maps, and field work are integrated in the GIS to generate an information layer. Database of this information layer is used to train, test, and validate the BPNN model. A three-layered BPNN with an input layer, two hidden layers, and one output layer is found to be optimal. The developed model demonstrates a promising result, and the prediction accuracy has been found to be 80 % in the field.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
Battiti R (1992) First and second order methods for learning: between steepest descent and Newton’s method. Neural Comput 4(2):141–166
Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16:427–445
Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, The Netherlands, pp 135–175
Chung CT, Chao RJ (2006) Application of back-propagation networks in debris flow prediction. Eng Geol 85:270–280
Chung CHF, Fabbri AG, Van Western CJ (1995) Multivariate regression analysis for landslide hazard zonation. In: Carrara A, Guzzetti F (eds) Geographical information system in assessing natural hazards. Kluwer, The Netherlands, pp 107–142
Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343
Ferna`ndez-Steeger TM, Rohn J, Czurda K (2002) Identification of landslide areas with neural nets for hazard analysis. In: Stemnerk J, Wagner P (eds) Landslides. Balkema, The Netherlands, pp 163–168
Genevois R, Tecca PR (1987) Probabilistic analysis of slopes stability: an application for hazard studies in the middle valley of the Tammaro River (southern Italy). Mem Soc Geol Ital 37:157–170 (in Italian)
Gill PE, Murray W, Wright MH (1981) Practical optimization. Academic Press, New York, pp 1–420
Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27
Gupta RP, Kunango DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10:330–341
Hammond C, Hall D, Miller S, Swetik P (1992) Level I stability analysis (LISA) documentation for version 2.0, General Technical Report INT-285, USDA Forest Service Intermountain Research Station
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey, p 842
Jade S, Sarkar S (1993) Statistical models for slope instability classification. Eng Geol 36:91–98
Jenks GF (1967) The data model concept in statistical mapping. Int Year Book Cartogr 7:186–190
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 85:3247–3366
Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the bayesian probability model. Environ Geol 43:120–131
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 71:289–302
Mayoraz F, Cornu T, Vuillet L (1996) Using neural networks to predict slope movements. In: Proceedings of VII international symposium on landslides. Trondheim, Balkema, Rotterdam, p 295–300
Palmstrom A (1982) The volumetric joint count—a useful and simple measure of the degree of rock mass jointing. In: IAEG Congress, New Delhi, p 221–228
Powell MJD (1977) Restart procedures for the conjugate gradient method. Math Program 12:241–254
Ramakrishanan D, Singh TN, Purwar N, Badre KS, Gulati A, Gupta S (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj Earthquake data, Gujarat, India. Comput Geosci 12:491–501
Ramakrishnan D, Ghosh MK, Vinuchandran R, Jeyaram A (2005) Probabilistic techniques, GIS and remote sensing in landslide hazard mitigation: a case study from Sikkim Himalayas, India. Geocarto Int 20(4):1–6
Ripley B (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge, p 416
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation in parallel distributed processing. Massachusetts Institute of Technology Press, Cambridge
Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69
Sarkar K, Gulati A, Singh TN (2008) Landslide susceptibility analysis using artificial neural networks and GIS in Luhri area, Himachal Pradesh. J Indian Landslides 1(1):11–20
Settle JJ, Briggs SS (1987) Fast maximum likelihood classification of remotely sensed imagery. Int J Remote Sens 8(5):723–734
Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using artificial neural networks technique. J Sci Ind Res 63(1):32–38
Singh TN, Gulati A, Dontha L, Bhardwaj V (2008) Evaluating cut slope failure by numerical analysis—a case study. Nat Hazards 47:263–279
Sinha S, Singh TN, Singh V, Verma AK (2009) Epoch determination for neural network by self organised map. Comput Geosci 14(1):199–206
Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679
Tiwari KC, Ganapathi S, Mehta A, Sharma S, Ramakrishnan D (2006) Landslide hazard zonation of Tawaghat—Jipti Route Corridor,Pithoragarh, Uttaranchal State: Using GIS and probabilistic technique approach. Proceedings of SPIE (Kogan F. Ed.) 6412:1–12
Widrow B, Jovitz MC, Jacobi GT, Goldstein G (1962) Generalization and information storage in networks of adaline’neurons’. In: Self organizing systems. Spartan Books, Washington DC, pp 435–461
Zhou W (1999) Verification of the nonparametric characteristics of back propagation neural networks for image classification. IEEE Trans Geosci Remote Sens 37:771–779
Zurada KJ (2006) Introduction to artificial neural systems. 10th edn. Jaico Publishing House, Mumbai, p 120
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ramakrishnan, D., Singh, T.N., Verma, A.K. et al. Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards 65, 315–330 (2013). https://doi.org/10.1007/s11069-012-0365-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-012-0365-4