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Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

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Abstract

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.

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References

  • Adhikari TL (2009) Disaster mitigation works along Narayanghat Mugling Highway. International seminar on hazard management for sustainable development

  • Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nat Res Res 1(1):39–50

    Article  Google Scholar 

  • Agterberg FP, Bonham-Carter GF, Cheng Q, Wright DF (1993) Weights of evidence modeling and weighted logistic regression for mineral potential mapping. In: Davis JC, Herzfeld UC (eds) Computers in geology, 25 years of progress. Oxford University Press, Oxford, pp 13–32

    Google Scholar 

  • Akgün A, Kincal C, Pradhan B (2011) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (West Turkey). Environ Monit Assess. doi:10.1007/s10661-011-2352-8

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44

    Article  Google Scholar 

  • Alexander DE (2008) A brief survey of GIS in mass movement studies, with reflections on theory and methods. Geomorphology 94:261–267

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. doi:10.1016/j.cageo.2012.3

    Article  Google Scholar 

  • Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277

    Google Scholar 

  • 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):73–81

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydro Sci Bull 24:43–69

    Google Scholar 

  • Binaghi E, Luzi L, Madella P, Pergalani F, Rampini A (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogr Eng Remote Sens 54:1585–1592

    Google Scholar 

  • Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. Statistical Applications in the Earth Science, Geological Survey of Canada Paper 89-9:171–183

    Google Scholar 

  • Bonham-Carter GF (1991) Integration of geoscientific data using GIS. In: Goodchild MF, Rhind DW, Maguire DJ (eds) Geographic information systems: principle and applications. Longman, London, pp 171–184

    Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. In: Bonham-Carter F (ed) Computer methods in the geosciences. Pergamon, Oxford, p 398

    Google Scholar 

  • Bonham-Carter GF (2002) Geographic information systems for geoscientist: modeling with GIS. Pergamon, New York, pp 302–334

    Google Scholar 

  • Bui HB, Nguyen Q, Nguyen VT (2008) GIS-based weight of evidence modeling for landslide susceptibility mapping at Jaechon area, Korea. International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, 4 pp

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2011) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. doi:10.1016/j.cageo.2011.10.031

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Landslide susceptibility assessment in the Hoa Binh province of Vietnam using artificial neural network. Geomorphology. doi:10.1016/ j.geomorph.2012.04.023

  • Bui DT, Pradhan B, Lofman O, Revhaug I (2012b) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and naïve Bayes models. Math Probl Eng 2012:1–26. doi:10.1155/2012/974638

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Spatial prediction of landslide hazards in Vietnam: a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Chen CH, Ke CC, Wang CL (2009) A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan. Environ Geol 57:723–733

    Article  Google Scholar 

  • Cheng K, Wei C, Chang S (2004) Locating landslides using multitemporal satellite images. Adv Space Res 33:296–301

    Google Scholar 

  • Chung CJ, Fabbri AG (1998) Three Bayesian prediction models for landslide hazard. In: Buccianti R, Potenza R, Nardi G (eds) Proceedings of International Association for Mathematical Geology 1998 Annual Meeting (IAMG 98), Ischia, Italy, 1998, pp 204–211

  • Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Chung CJ, Fabbri A (2005) Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. In: Glade T, Anderson MG, Crozier MJ (eds) Landslide hazard and risk. Wiley, New York, pp 139–177

    Google Scholar 

  • Carranza EJM, Castro O (2006) Predicting lahar-inundation zones: case study in West Mount Pinatubo, Philippines. Nat Hazards 37:331–372

    Article  Google Scholar 

  • Carranza EJM, Hale M (2001) Geologically constrained fuzzy mapping of gold mineralization potential, Baguio District, Philippines. Nat Resour Res 10:125–136

    Article  Google Scholar 

  • Carranza EJM, Hale M (2002) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol R 22:117–132

    Article  Google Scholar 

  • Carranza EJM, Hale M (2000) Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Nat Res Res 9:237–253

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008a) Predictive modeling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of evidence. Geomorphology 102(3–4):496–510

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008b) GIS-based weights-of-evidence modeling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):314–324

    Article  Google Scholar 

  • Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87

    Article  Google Scholar 

  • Daneshfar B, Benn K (2002) Spatial relationships between natural seismicity and faults, southeastern Ontario and north-central New York State. Tectonophysics 353:31–44

    Google Scholar 

  • Dangol V (2000) Slope instability study and hazard mapping of Syangja district, Western Nepal. Unpublished Report, ICIMOD, Katmandu, Nepal

  • Dangol V, Upreti BN, Dhital MR, Wagner A, Bhattarai TN, Bhandari AN, Pant SR, Sharma MP (1993) Engineering geological study of a proposed road corridor in Eastern Nepal. Bull Dept Geol Tribhuvan University 3(1):91–107

    Google Scholar 

  • Deoja B, Dhital MR, Thapa B, Wagner A (1991) Mountain risk engineering handbook. ICIMOD, Kathmandu, 857 pp

    Google Scholar 

  • Devkota K, Regmi AD, Pourghasemi HR, Yishida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2012) 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. doi:10.1007/s11069-012-0347-6

  • Dhital MR, Upreti BN, Dangol V, Bhandari AN, Bhattarai TN (1991) Geological engineering methods applied in mountain road survey: an example from Baitadi–Darchula Road Project (Nepal). J Nepal Geol Soc 7:49–67

    Google Scholar 

  • Dhital MR (2005) Landslide investigation and mitigation in Himalayas: focus on Nepal. Proceedings of the International Symposium “Landslide Hazard in Orogenic Zone from the Himalaya to Island Arc in Asia,” 25–26 September, Kathmandu, Nepal, pp 1–15

  • DWIDP (2009) The study on disaster risk management for Narayanghar–Mugling Highway. Unpublished report

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250

    Article  Google Scholar 

  • Ganser A (1964) Geology of the Himalaya. Inter Science John Wiley, London

    Google Scholar 

  • Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81:65–83

    Article  Google Scholar 

  • Gupta RP, Joshi BC (1990) Landslide hazard zonation using the GIS approach—a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184

    Article  Google Scholar 

  • Hagen T (1969) Report on the geological survey of Nepal preliminary reconnaissance: Zürich, Mémoires de la soc. Helvétique des sci. naturelles, p 185

  • 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 85:347–366

    Google Scholar 

  • Kanungo DP, Arora MK, Gupta RP, Sarkar S (2008) Landslide risk assessment using danger pixel and fuzzy concepts in Darjeeling Himalayas. Landslides 5:407–416

    Google Scholar 

  • Kolat C, Doyuran V, Ayday C, Suzen LM (2006) Preparation of a geotechnical microzonation model using geographical information systems based on multicriteria decision analysis. Eng Geol 87 (3–4):241–255

    Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S (2007a) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Proc & Landf 32:2133–2148

    Article  Google Scholar 

  • Lee S (2007b) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623

    Article  Google Scholar 

  • Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Mathematical Geol 38:199–220

    Article  Google Scholar 

  • Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–667

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41

    Article  Google Scholar 

  • Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Nat Hazards Earth Syst Sci 10:1851–1864

    Google Scholar 

  • Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci 92(5):628–638

    Google Scholar 

  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400

    Article  Google Scholar 

  • 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 61:221–236. doi:10.1016/j.jseaes.2012.10.005

  • Moore ID, Grayson RB (1991) Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resour Res 27(6):1171–1191

    Google Scholar 

  • Muthu K, Petrou M, Tarantino C, Blonda P (2008) Landslide possibility mapping using fuzzy approaches. IEEE Trans Geosci Remote Sens 46(4):1253–126

    Article  Google Scholar 

  • Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Env 58:275–287

    Article  Google Scholar 

  • Nefeslioglu H, Sezer E, Gokceoglu C, Bozkir A, Duman T (2010a) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering 2010:Article ID 901095. doi:10.1155/2010/901095

  • Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010b) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering 2012:1–15. doi:10.1155/2010/901095

  • Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86:12–24

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 7(9):1264–1276. doi:10.1016/j.cageo.2010.10.012

    Article  Google Scholar 

  • Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343

    Google Scholar 

  • Oyagi N (1984) Landslides in weathered rocks and residuals soils in Japan an surrounding areas: state-of-the-art report. 4th International Symposium on Landslides, Toronto, pp 1–131

  • Pantha BR, Yatabe R, Bhandari NP (2008) GIS-based landslide susceptibility zonation for roadside slope repair and maintenance in the Himalayan region. Episodes 31(4):384–291

    Google Scholar 

  • Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012a) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed Iran. Arab J Geosci. doi:10.1007/s12517-012-0532-7

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012b) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk. doi:10.1080/19475705.2012.662915

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012c) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996. doi:10.1007/s11069-012-0217-2

  • Pourghasemi HR, Mohammady M, Pradhan B (2012d) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84. doi:10.1016/j.catena.2012.05.005

    Article  Google Scholar 

  • Pourghasemi HR, Gokceoglu C, Pradhan B, Deylami Moezzi K (2012e) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49. doi:10.1007/978-3-642-25495-6-2

  • Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2012f) Landslide susceptibility mapping using support vector machine and GIS. J Earth Syst Sci 61(15):221–236

    Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012g) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. Appl Mech Mater 225:486–491

    Google Scholar 

  • Pradhan B (2010a) Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256

    Article  Google Scholar 

  • Pradhan B (2010b) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Syst 3:370–381

    Google Scholar 

  • Pradhan B (2010c) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320

    Article  Google Scholar 

  • Pradhan B (2012) 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 51:350–365. doi:10.1016/j.cageo.2012.08.023

  • Pradhan B (2011a) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat 18(3):471–493. doi:10.1007/s10651-010-0147-7

    Article  Google Scholar 

  • Pradhan B (2011b) 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):329–349

    Article  Google Scholar 

  • Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709

    Article  Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. I J Physical Sci 3(11):1–15

    Google Scholar 

  • Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126

    Google Scholar 

  • Pradhan B, Pirasteh S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3(2):26–34

    Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010a) Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogrammetrie, Fernerkundung, Geoinformation (PFG) 1(1):17–32

  • Pradhan B, Lee S, Buchroithner MF (2010b) A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Comput Environ & Urban Syst 34:216–235

    Article  Google Scholar 

  • Pradhan B, Chaudhari A, Adinarayana J, Buchroithner MF (2012) Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environ Monit Assess 184(2):715–727. doi:10.1007/s10661-011-1996-8

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Dhital MR, Devkota K (2012a) Effect of rock weathering, clay mineralogy, and geological structures in the formation of large landslide, a case study from Dumre Besei landslide, Lesser Himalaya Nepal. Landslides. doi:10.1007/s10346-011-0311-7

  • Regmi AD, Yoshida K, Nagata H, Pradhan AMS, Pradhan B, Pourghasemi HR (2012b) The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal Himalaya. Nat Hazards. doi:10.1007/s11069-012-0497-6

  • Regmi NR, Giardino JR, Vitek JD (2010a) Assessing susceptibility to landslides: using models to understand observed changes in slopes. Geomorphology 112:25–38

    Article  Google Scholar 

  • Regmi NR, Giardino JR, Vitek JD (2010b) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187

    Article  Google Scholar 

  • Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 109:108–121

    Article  Google Scholar 

  • Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 23:357–369

    Article  Google Scholar 

  • Saha AK, Gupta RP, Sarkar I, Arora KM, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas. Landslides 2(1):61–69

    Article  Google Scholar 

  • 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(7):8208–8219

    Article  Google Scholar 

  • Sharma M, Kumar R (2008) GIS-based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, H.P., India. B Eng Geol Environ 67:129–137

    Article  Google Scholar 

  • Stöcklin J (1980) Geology of Nepal and its regional frame. J Geol Soc London 137:1–34

    Article  Google Scholar 

  • Stöcklin J, Bhattarai KD (1978) Geology of the Kathmandu area and central Mahabharat range, Nepal Himalaya. Unpublished Report of the Department of Mines and Geology/UNDP, 86 p

  • Upreti BN (1999) An overview of the stratigraphy and tectonics of the Nepal Himalaya. J Asian Earth Sci 17:577–606

    Article  Google Scholar 

  • 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(29):1101–1114

    Google Scholar 

  • van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS based hazard zonation. Geol Rundsch 86:404–414

    Article  Google Scholar 

  • van Westen CJ, Seijmonsbergen AC, Mantovani F (1999) Comparing landslide hazard maps. Nat Hazards 20(2/3):137–158

    Article  Google Scholar 

  • van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Eng Geol 108:237–251

    Article  Google Scholar 

  • Wan S, Lei TC, Huang PC, Chou TY (2008) Knowledge rules of debris flow event: a case study for investigation ChenYu Lan River Taiwan. Eng Geol 98:102–114

    Article  Google Scholar 

  • Wan S, Lei TC (2009) A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan. Knowl Based Syst 2(8):580–588

    Google Scholar 

  • Wan S, Lei TC, Chou TY (2009) Anovel data mining technique of analysis and classification for landslide problems. Nat Hazards 52:211–230

    Google Scholar 

  • Xiao CC, Tian Y, Shi WZ, Guo QH, Wu L (2010) A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen. Sci China Tech Sci 53(1):75–84. doi:10.1007/s11431-010-3219-x

    Article  Google Scholar 

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582

    Google Scholar 

  • Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using decision tree. Eng Geol 116:274–283

    Article  Google Scholar 

  • Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. B Eng Geol Environ 68:297–306

    Article  Google Scholar 

  • Yilmaz I (2009b) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Yilmaz I (2010a) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836

    Article  Google Scholar 

  • Yilmaz I (2010b) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60:505–519

    Article  Google Scholar 

  • Zhu C, Wang X (2009) Landslide susceptibility mapping: a comparison of information and weights-of evidence methods in Three Gorges Area. Int Conf Environ Sci Inf Appl Technol 2009:342–346. doi:10.1109/ESIAT.2009.187

    Google Scholar 

  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi:10.1007/s12517-012-0610-x

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Acknowledgments

The authors express their gratitude to MEXT (Ministry of Education, Culture, Sports, Science and Technology, Tokyo, Japan) for funding the present study. Dr. Michel Calvelo, Mr. Pravin Kayasta, and Mr. Ananta Man Shingh are sincerely acknowledged for their great help during the process of writing this paper. Also, the authors would like to thank two anonymous reviewers for their helpful comments on the previous version of the manuscript

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Authors and Affiliations

  1. Department of Geology, Faculty of Science, Shinshu University, Asahi 3-1-1, Matsumoto, 3908621, Japan

    Amar Deep Regmi & Kohki Yoshida

  2. Department of Geology, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu, 702-701, South Korea

    Krishna Chandra Devkota

  3. Geospatial Information Science Research Centre (GISRC), Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, 43400, Malaysia

    Biswajeet Pradhan

  4. College of Natural Resources and Marine Sciences, Tarbiat Modares University (TMU), Mazandaran, Iran

    Hamid Reza Pourghasemi

  5. Department of Earth Sciences, Faculty Science, Okayama University, Tsushimanaka 3-1-1, Kita-ku, Okayama-shi, Okayama, 700-8530, Japan

    Takashi Kumamoto

  6. Geological Engineering Department, Black Sea Technical University, Trabzon, Turkey

    Aykut Akgun

Authors
  1. Amar Deep Regmi
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  2. Krishna Chandra Devkota
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  3. Kohki Yoshida
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  4. Biswajeet Pradhan
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  5. Hamid Reza Pourghasemi
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  6. Takashi Kumamoto
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  7. Aykut Akgun
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Correspondence to Biswajeet Pradhan.

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Regmi, A.D., Devkota, K.C., Yoshida, K. et al. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7, 725–742 (2014). https://doi.org/10.1007/s12517-012-0807-z

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  • DOI: https://doi.org/10.1007/s12517-012-0807-z

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