Dynamic association of slope movements in the Uttarakhand Himalaya: a critical review on the landslide susceptibility assessment

References

• Ada M, San BT. 2018. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Nat Hazards. 90(1):237–263. doi: 10.1007/s11069-017-3043-8.
   Web of Science ®Google Scholar
• Aghdam N, 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(7):1–20.
   Web of Science ®Google Scholar
• Akgun A, Dag S, Bulut F. 2008. Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol. 54(6):1127–1143. doi: 10.1007/s00254-007-0882-8.
   Web of Science ®Google Scholar
• Akgün A, Türk N. 2011. Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region, NW Turkey. Comput Geosci. 37(9):1515–1524. doi: 10.1016/j.cageo.2010.09.006.
   Web of Science ®Google Scholar
• Akgun A. 2012. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides. 9(1):93–106. doi: 10.1007/s10346-011-0283-7.
   Web of Science ®Google Scholar
• Allen S, Huggel C. 2013. Extremely warm temperatures as a potential cause of recent high mountain rock fall. Global Planet Change. 107:59–69. doi: 10.1016/j.gloplacha.2013.04.007.
   Web of Science ®Google Scholar
• Allen SA. 1984. Types of land subsidence. In: Poland JF, editor. Guidebook to studies of land subsidence due to ground-water withdrawal. Paris: United Nations Educational Scientific and Cultural Organization; p. 291–305.
   Google Scholar
• Althuwaynee OF, Pradhan B, Mahmud AR, Yusoff ZM. 2012. Prediction of slope failures using bivariate statistical based index of entropy model. IEEE Colloquium on Humanities, Science and Engineering (CHUSER). doi: 10.1109/chuser.2012.6504340.
   Google Scholar
• Althuwaynee OF, Pradhan B, Park HJ, Lee JH. 2014a. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 114:21–36. doi: 10.1016/j.catena.2013.10.011.
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Park HJ, Lee JH. 2014b. A novel ensemble decision tree based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides. 11(6):1063–1078. doi: 10.1007/s10346-014-0466-0.
   Web of Science ®Google Scholar
• 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(3):559–572., doi: 10.1080/0143116031000156819.
   Web of Science ®Google Scholar
• Auden JB. 1935. Traverses in the Himalaya. Rec Geol Surv Ind. 69:123–167.
   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. doi: 10.1007/s10346-003-0006-9.
   Web of Science ®Google Scholar
• Bai X, Jian J, He S, Liu W. 2019. Dynamic process of the massive Xinmo landslide, Sichuan (China), from joint seismic signal and morphodynamic analysis. Bull Eng Geol Environ. 78(5):3269–3279. doi: 10.1007/s10064-018-1360-0.
   Web of Science ®Google Scholar
• Barnard PL, Owen LA, Sharma MC, Finkel RC. 2001. Natural and human-induced landsliding in the Garhwal Himalaya of Northern India. Geomorphology. 40(1–2):21–35. doi: 10.1016/S0169-555X(01)00035-6.
   Web of Science ®Google Scholar
• Batar AK, Watanabe T. 2021. Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the Indian Himalayan Region: recent developments, gaps, and future directions. ISPRS Int J Geo-Inf. 10(3):114. doi: 10.3390/ijgi10030114.
   Web of Science ®Google Scholar
• Bhargavi P, Jyothi S. 2009. Applying naive bayes data mining technique for classification of agricultural land soils.
   Google Scholar
• Bhattacharyya P, DasGupta S, Das S, Paul S. 2020. Landslide susceptibility analysis: a case study of Nainital municipal area. 23 November, Preprint (Version 1) available at Research Square. doi: 10.21203/rs.3.rs-106891/v1.
   Google Scholar
• Binh Thai P, Dieu TB, Indra P. 2017. Application of classification and regression trees for spatial prediction of rainfall induced shallow landslides in the Uttarakhand Area (India) using GIS. In: Climate change, extreme events and disaster risk reduction. Berlin, Heidelberg: Springer; p. 159–170.
   Google Scholar
• Birkinshaw SJ, James P, Ewen J. 2010. Graphical user interface for rapid setup of SHETRAN physically-based river catchment model. Environ Modell Softw. 25(4):609–610. doi: 10.1016/j.envsoft.2009.11.011.
   Web of Science ®Google Scholar
• Brenning A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci. 5(6):853–862. doi: 10.5194/nhess-5-853-2005.
   Web of Science ®Google Scholar
• Brenning A. 2008. Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. In: Böhner J, Blaschke T, Montanarella L, Böhner J, editors. SAGA—seconds out. Hamburger Beiträge zur Physischen Geographie und Landschafts Ökologie. Vol. 19. Universität Hamburg, Institut für Geographie; p. 23–32, 410.
   Google Scholar
• Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. 2012. Landslide susceptibility mapping at HoaBinh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. 45:199–211. doi: 10.1016/j.cageo.2011.10.031.
   Web of Science ®Google Scholar
• Calvello M, Cascini L, Mastroianni S. 2013. Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units. Geomorphology. 182:33–48. doi: 10.1016/j.geomorph.2012.10.026.
   Web of Science ®Google Scholar
• Campbell RH. 1975. Soil slips, debris flows, and rainstorms in the Santa Monica mountains and vicinity, Southern California. U.S. Geological Survey Professional Paper 851; p. 51.
   Google Scholar
• Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landforms. 16(5):427–445. doi: 10.1002/esp.3290160505.
   Web of Science ®Google Scholar
• Champati Ray PK, Dimri S, Lakhera RC, Sati S. 2007. Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides. 4(2):101–111. doi: 10.1007/s10346-006-0068-6.
   Web of Science ®Google Scholar
• Chang KT, Merghadi A, Yunus AP, Pham BT, Dou J. 2019. Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep. 9(1):12296. doi: 10.1038/s41598-019-48773-2.
   PubMedGoogle Scholar
• Chang M, Tang C, Xia C, Fang Q. 2016. Spatial distribution analysis of landslides triggered by the 2013-04-20 Lushan earthquake, China. Earthq Eng Eng Vib. 15(1):163–171. doi: 10.1007/s11803-016-0313-5.
   Web of Science ®Google Scholar
• Chauhan S, Sharma M, Arora MK, Gupta NK. 2010. Landslide susceptibility zonation through ratings derived from artificial neural network. Int J Appl Earth Obs Geoinf. 12(5):340–350. doi: 10.1016/j.jag.2010.04.006.
   Web of Science ®Google Scholar
• Chen T, Niu R, Jia X. 2016. A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci. 75(10):1–16. doi: 10.1007/s12665-016-5317-y.
   Web of Science ®Google Scholar
• Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A-X, Pei X, Duan Z. 2018. Duan landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ. 626:1121–1135. doi: 10.1016/j.scitotenv.2018.01.124.
   PubMed Web of Science ®Google Scholar
• Chen W, Shahabi H, Shirzadi A, Hong H, Akgun A, Tian Y, Liu J, Zhu AX, Li S. 2019. Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernelogistic regression classifier for landslide susceptibility modeling. Bull Eng Geol Environ. 78(6):4397–4419. doi: 10.1007/s10064-018-1401-8.
   Web of Science ®Google Scholar
• Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N. 2017. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics Nat Hazards Risk. 8(2):1955–1977. doi: 10.1080/19475705.2017.1401560.
   Web of Science ®Google Scholar
• Choi J, Lee YK, Lee M, Kim K, Park Y, Kim S, Goo S, Cho M, Sim J, Won JS. 2011. Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). IEEE International Geoscience and Remote Sensing Symposium. New York (NY): IEEE; p. 1989–1992.
   Google Scholar
• Chung CJF, Fabbri AG. 2003. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 30(3):451–472. doi: 10.1023/B:NHAZ.0000007172.62651.2b.
   Web of Science ®Google Scholar
• Ciampalini A, Raspini F, Lagomarsino D, Catani F, Casagli, N, 2016. Landslide susceptibility map refinement using PSInSAR data. Rem Sens Environ. 184:302–315. doi: 10.1016/j.rse.2016.07.018.
   Web of Science ®Google Scholar
• Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gomez-Gutierrez A, Rotigliano E, Agnesi V. 2015. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology. 242:49–64. doi: 10.1016/j.geomorph.2014.09.020.
   Web of Science ®Google Scholar
• [C3S] Copernicus Climate Change Service. 2017. ERA5: fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS). (date of access). https://cds.climate.copernicus.eu/cdsapp#!/home.
   Google Scholar
• Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, et al. 2013. Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ. 73(2):209–263. doi: 10.1007/s10064-013-0538-8.
   Web of Science ®Google Scholar
• Crozier MJ. 1986. Landslides: causes, consequences & environment. London: Croom Helm Publication.
   Google Scholar
• Crozier MJ. 2010. Deciphering the effect of climate change on landslide activity: a review. Geomorphology. 124(3–4):260–267. doi: 10.1016/j.geomorph.2010.04.009.
   Web of Science ®Google Scholar
• Das SR, Ganguli P. 2022. Predictability of rainfall induced-landslides: the case study of Western Himalayan Region. EGU Sphere :1–32. doi: 10.5194/egusphere-2022-243.
   Google Scholar
• Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. 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. 65(1):135–165. doi: 10.1007/s11069-012-0347-6.
   Web of Science ®Google Scholar
• Devoli G, Strauch W, Chávez G, Høeg K. 2007. A landslide database for Nicaragua: a tool for landslide-hazard management. Landslides. 4(2):163–176., doi: 10.1007/s10346-006-0074-8.
   Web of Science ®Google Scholar
• Dewey JF, Burke K. 1973. Tibetan, Variscan and Precambrian basement reactivation: products of continental collision. J Geol. 81(6):683–692. doi: 10.1086/627920.
   Web of Science ®Google Scholar
• Dickson ME, Perry GLW. 2016. Perry identifying the controls on coastal cliff landslides using machine-learning approaches. Environ Model Softw. 76:117–127. doi: 10.1016/j.envsoft.2015.10.029.
   Web of Science ®Google Scholar
• Dikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM. 2020. Rainfall induced landslide studies in Indian Himalayan region: a critical review. Appl Sci. 10(7):2466. doi: 10.3390/app10072466.
   Google Scholar
• Dikshit A, Satyam N. 2017. Rainfall thresholds for the prediction of landslides using empirical methods in Kalimpong, Darjeeling, India. Workshop on Advances in Landslide Understanding, JTC1; Barcelona; p. 255–259.
   Google Scholar
• Dimri AP, Chevuturi A, Niyogi D, Thayyen RJ, Ray K, Tripathi SN, Pandey AK, Mohanty UC. 2017. Cloudbursts in Indian Himalayas: a review. Earth Sci Rev. 168:1–23. volumedoi: 10.1016/j.earscirev.2017.03.006.
   Web of Science ®Google Scholar
• Ding Q, Chen W, Hong H. 2016. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int. 32:6, 619–639. doi: 10.1080/10106049.2016.1165294.
   Web of Science ®Google Scholar
• Ellen SD, Mark RK, Cannon SH, Knifong DL. 1993. Map of debris-flow hazard in the Honolulu District of Oahu, Hawaii. U.S. Geological Survey Open-File Report 93-2 13, 25 pp. Entropy. 20(11):884.
   Google Scholar
• Ellen SD, Wieczorek GF, Brown WM III, Herd DG. 1988. Introduction. In: Ellen SD, Wieczorek GF, editors. Landslides, floods, and marine effects of the storm of January 3–5, 1982, in the San Francisco Bay Region, California—Introduction, U.S. Geological Survey Professional Paper 1434; p. 1–5.
   Google Scholar
• Ermini L, Casagli N. 2003. Prediction of the behavior of landslide dams using a geomorphological dimensionless index. Earth Surf Process Landforms. 28(1):31–47. doi: 10.1002/esp.424.
   Web of Science ®Google Scholar
• Ewen J, Parkin G, O’Connell P. 2000. SHETRAN: distributed river basin flow and transport modeling system. J Hydrol Eng. 5(3):250–258. doi: 10.1061/(ASCE)1084-0699(2000)5:3(250).
   Web of Science ®Google Scholar
• Fan W, Wei XS, Cao YB, Zheng B. 2017. Landslide susceptibility assessment using the certainty factor and analytic hierarchy process. J Mt Sci. 14(5):906–925. doi: 10.1007/s11629-016-4068-2.
   Web of Science ®Google Scholar
• Fan X, Scaringi G, Korup O, West AJ, van Westen CJ, Tanyas H, Hovius N, Hales TC, Jibson RW, Allstadt KE, et al. 2019. Earthquake‐induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys. 57(2):421–503. doi: 10.1029/2018RG000626.
   Web of Science ®Google Scholar
• Feizizadeh B,Blaschke T. 2012. Comparing GIS-multicriteria decision analysis for landslide susceptibility mapping for the lake basin, Iran. 2012 IEEE International Geoscience and Remote Sensing Symposium. doi: 10.1109/igarss.2012.6352388.
   Google Scholar
• Feizizadeh B, Blaschke T, Nazmfar H. 2014. GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth. 7(8):688–708. doi: 10.1080/17538947.2012.749950.
   Web of Science ®Google Scholar
• Feizizadeh B. 2013. Integrating GIS based fuzzy set theory in multicriteria evaluation methods for landslide susceptibility mapping. Int J Geoinformatics. 9:49–57.
   Google Scholar
• Feizizadeh B, Blaschke T, Nazmfar H, Rezaei Moghaddam M. 2013. Landslide susceptibility mapping for the Urmia Lake basin, Iran: a multi-criteria evaluation approach using GIS. Int J Environ Res. 7(2):319–336. doi: 10.22059/ijer.2013.610.
   Web of Science ®Google Scholar
• Felicísimo A, Cuartero A, Remondo J, Quirós E. 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. 10(2):175–189. doi: 10.1007/s10346-012-0320-1.
   Web of Science ®Google Scholar
• Fischer L, Huggel C, Kääb A, Haeberli W. 2013. Slope failures and erosion rates on a glacierized high‐mountain face under climatic changes. Earth Surf Process Landforms. 38(8):836–846. doi: 10.1002/esp.3355.
   Web of Science ®Google Scholar
• García-Rodríguez MJ, Malpica JA, Benito B, Díaz M. 2008. Susceptibility assessment of earthquake triggered landslides in El Salvador using logistic regression. Geomorphology. 95(3–4):172–191. doi: 10.1016/j.geomorph.2007.06.001.
   Web of Science ®Google Scholar
• Goetz JN, Brenning A, Petschko H, Leopold P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci. 81:1–11. doi: 10.1016/j.cageo.2015.04.007.
   Web of Science ®Google Scholar
• Goetz JN, Cabrera R, Brenning A, Heiss G, Leopold P. 2015. Modelling landslide susceptibility for a large geographical area using weights of evidence in lower Austria, Austria. In: Engineering geology for society and territory. Vol. 2. Cham, Switzerland: Springer International Publishing; p. 927–930. doi: 10.1007/978-3-319-09057-3_160.
   Google Scholar
• Goetz JN, Guthrie RH, Brenning A. 2011. Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology. 129(3–4):376–386. doi: 10.1016/j.geomorph.2011.03.001.
   Web of Science ®Google Scholar
• Gruber S, Hoelzle M, Haeberli W. 2004. Permafrost thaw and destabilization of Alpine rock walls in the hot summer of 2003. Geophys Res Lett. 31(13):L13504. doi: 10.1029/2004GL020051.
   Web of Science ®Google Scholar
• GSI and NRSC. 2012. National geomorphological and Lineament mapping on 1:50,000 scale, Natural Resources Census Project. Hyderabad: National Remote Sensing Centre, ISRO.
   Google Scholar
• GSI. 2021. Geological map, Geological Survey of India. Government of India, Kolkata, India. Geologists. 27(1):73–78.
   Google Scholar
• Guettouche MS. 2013. Modeling and risk assessment of landslides using fuzzy logic. Application on the slopes of the Algerian Tell (Algeria). Arab J Geosci. 6(9):3163–3173. doi: 10.1007/s12517-012-0607-5.
   Web of Science ®Google Scholar
• Gupta K, Satyam N. 2022. Seismically induced Landslide hazard assessment based on the spatial distribution of the slope strength demand in the Western Himalayas. EGU General Assembly; May; Vienna, Austria, EGU22-7328; p. 23–27. doi: 10.5194/egusphere-egu22-7328.
   Google Scholar
• Guzzetti F,Mondini AC,Cardinali M,Fiorucci F,Santangelo M,Chang KT. 2012. Landslide inventory maps: new tools for an old problem. Earth-Science Reviews 112(1-2):42–66. doi: 10.1016/j.earscirev.2012.02.001. ISSN 0012–8252.
   Web of Science ®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(1–4):181–216. doi: 10.1016/S0169-555X(99)00078-1.
   Web of Science ®Google Scholar
• Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M. 2006. Estimating the quality of landslide susceptibility models. Geomorphology. 81(1–2):166–184. doi: 10.1016/j.geomorph.2006.04.007.
   Web of Science ®Google Scholar
• Guzzetti F. 2002. Landslide hazard assessment and risk evaluation: limits and prospectives. Proceedings of the 4th EGS Plinius Conference; Mallorca, Spain; p. 2–4.
   Google Scholar
• Hammond C, Hall D, Miller S, Swetick P. 1992. Level I Stability Analysis (LISA) Documentationfor Version 2.0. General Technical Report INT-285, U. S. Dept. of Agriculture, Forest Service, Intermountain Research Station, Moscow, ID; p. 130.
   Google Scholar
• Heim A, Gansser A. 1939. Central Himalaya geological observations of Swiss.
   Google Scholar
• Holland TH. 1908. On the occurrence of striated boulders in the Blaini Formation of Simla with a discussion on the geological age of the beds. Rec Geol Surv India. 81:129–185.
   Google Scholar
• Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, Chen W, Ahmad BB. 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena. 163:399–413. doi: 10.1016/j.catena.2018.01.005.
   Web of Science ®Google Scholar
• Huggel C, Salzmann N, Allen S, Caplan-Auerbach J, Fischer L, Haeberli W, Larsen C, Schneider D, Wessels R. 2010. Recent and future warm extreme events and high mountain slope stability. Philos Trans A Math Phys Eng Sci. 368(1919):2435–2459. doi: 10.1098/rsta.2010.0078.
   PubMed Web of Science ®Google Scholar
• Hungr O, McDougall S. 2009. Two numerical models for landslide dynamic analysis. Comput Geosci. 35(5):978–992. doi: 10.1016/j.cageo.2007.12.003.
   Web of Science ®Google Scholar
• Hutchinson CM. 1991. A preliminary landslide hazard zonation of the undercliff of the Isle of Wight. London: Slope Stability Engineering, Thomas Telford; p. 197–205.
   Google Scholar
• Hutchinson JN. 1995. Keynote paper: landslide hazard assessment. In: Bell DH, editor. Landslides. Rotterdam: Balkema; p. 1805–1841.
   Google Scholar
• Jain AK. 1971. Stratigraphy and tectonics of lesser Himalayan region of Uttarkashi, Garhwal Himalaya. Himal Geol. 1:25–58.
   Google Scholar
• Jain S, Khosa R, Gosain AK. 2022. Impact of landslide size and settings on landslide scaling relationship: a study from the Himalayan regions of India. Landslides. 19(2):373–385. doi: 10.1007/s10346-021-01794-3.
   Web of Science ®Google Scholar
• Javed IT, Ningsheng C, Regmi AD, Jun L. 2017. Spatial distribution analysis and susceptibility mapping of landslides triggered before and after mw7.8 Gorkha earthquake along upper Bhote Koshi, Nepal. Arab J Geosci. 10(13):1–24.
   Web of Science ®Google Scholar
• Jibson RW. 1989. Debris flows in southern Puerto Rico. In: Schultz AP, Jibson RW, editors. Landslide processes of the eastern United States and Puerto Rico. Geological Society of America Special Paper 236; p. 29–56. doi: 10.1130/SPE236-p29.
   Google Scholar
• Jomelli V, Pech VP, Chochillon C, Brunstein D. 2004. Geomorphic variations of debris flows and recent climatic change in the French Alps. Clim Change. 64(1/2):77–102. doi: 10.1023/B:CLIM.0000024700.35154.44.
   Web of Science ®Google Scholar
• Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hubl J. 2019. Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ. 653:801–814. doi: 10.1016/j.scitotenv.2018.10.431.
   PubMed Web of Science ®Google Scholar
• Kainthura P, Sharma N. 2022. Hybrid machine learning approach for landslide prediction, Uttarakhand, India. Sci Rep. 12(1):20101. doi: 10.1038/s41598-022-22814-9.
   PubMed Web of Science ®Google Scholar
• Kamp U, Growley BJ, Khattak GA, Owen LA. 2008. GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology. 101(4):631–642. doi: 10.1016/j.geomorph.2008.03.003.
   Web of Science ®Google Scholar
• Kanungo DP, Arora MK, Sarkar S, Gupta RP. 2009. A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation. Georisk. 3(1):30–43. doi: 10.1080/17499510802541417.
   Google Scholar
• Kanungo DP, Sarkar S, Sharma S. 2011. Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards. 59(3):1491–1512. doi: 10.1007/s11069-011-9847-z.
   Web of Science ®Google Scholar
• Kanungo DP, Sharma S. 2014. Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India. Landslides. 11(4):629–638. doi: 10.1007/s10346-013-0438-9.
   Web of Science ®Google Scholar
• Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, Bevington A, Fielding EJ, Fujita K, Geertsema M, Miles ES, Steiner J, et al. 2016. Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science. 351(6269):aac8353. doi: 10.1126/science.aac8353.
   PubMed Web of Science ®Google Scholar
• Kayastha P, Dhital MR, De Smedt F. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat Hazards. 63(2):479–498. doi: 10.1007/s11069-012-0163-z.
   Web of Science ®Google Scholar
• Kayastha P. 2015. Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin, East Nepal. Arab J Geosci. 8(10):8601–8613. doi: 10.1007/s12517-015-1831-6.
   Web of Science ®Google Scholar
• Khanduri S. 2017. Landslide hazard around Mussoorie: the Lesser Himalayan tourist destination of Uttarakhand, India. J Geogr Nat Disast. 7(2):1000200. doi: 10.4172/2167-0587.1000200.
   Google Scholar
• Khandurı S. 2022. Disastrous events of 2021 in Uttarakhand province of India: causes, consequences and suggestions for disaster risk reduction (DRR). IJESKA. 4(2):178–188.
   Google Scholar
• Kornejady A, Ownegh M, Bahremand A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena. 152:144–162. doi: 10.1016/j.catena.2017.01.010.
   Web of Science ®Google Scholar
• Kumar R, Anbalagan R. 2016. Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India. 87(3):271–286. doi: 10.1007/s12594-016-0395-8.
   Web of Science ®Google Scholar
• Larsen MC, Torres Sanchez AJ. 1992. Landslides triggered by hurricane Hugo in eastern Puerto Rico, September 1989. Carib J Sci. 28(3–4):113–125.
   Google Scholar
• Lee JM. 2004. An efficient algorithm for Naive Bayes with matrix transposition.
   Google Scholar
• Lee S, Choi J, Min K. 2004. Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Rem Sens. 25(11):2037–2052. doi: 10.1080/01431160310001618734.
   Web of Science ®Google Scholar
• Lee S, Choi J. 2004. Landslide susceptibility mapping using GIS and the weight-of-evidence model. Int J Geogr Inf Sci. 18(8):789–814. doi: 10.1080/13658810410001702003.
   Web of Science ®Google Scholar
• LeForte P. 1975. Himalayas: the collided range. Present Knowledge of the continental arc. Am J Sci. 275A:1–44.
   Google Scholar
• Li M, Ge D, Liu B, Zhang L, Wang Y, Guo X, Wang Y, Zhang D. 2019. Research on development characteristics and failure mechanism of land subsidence and ground fissure in Xi’an, monitored by using time-series SAR interferometry. Geomat Nat Haz Risk. 10(1):699–718. doi: 10.1080/19475705.2018.1542350.
   Web of Science ®Google Scholar
• Liu CN. 2008. Landslide hazard mapping using Monte Carlo simulation–a case study in Taiwan. In: Liu H, Deng A, Chu J, editors. Geotechnical engineering for disaster mitigation and rehabilitation. Berlin; Heidelberg: Springer. doi: 10.1007/978-3-540-79846-0_14.
   Google Scholar
• Liu X, Miao C. 2018. Large-scale assessment of landslide hazard, vulnerability and risk in China. Geomat Nat Hazards Risk. 9(1):1037–1052. doi: 10.1080/19475705.2018.1502690.
   Web of Science ®Google Scholar
• Mandal S, Mandal K. 2018. Bivariate statistical index for landslide susceptibility mapping in the Rorachu River Basin of eastern Sikkim Himalaya, India. Spat Inf Res. 26(1):59–75. doi: 10.1007/s41324-017-0156-9.
   Web of Science ®Google Scholar
• Marin RJ, Mattos ÁJ. 2020. Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin. Georisk. 14(3):192–205. doi: 10.1080/17499518.2019.1633582.
   Google Scholar
• Mathewson CC, Keaton JR, Santi PM. 1990. Role of bedrock ground water in the initiation of debris flows and sustained post-storm stream discharge. Bull Assoc Eng. XXVII(1):73–83. doi: 10.2113/gseegeosci.xxvii.1.73.
   Google Scholar
• McDougall S, Hungr O. 2005. Dynamic modelling of entrainment in rapid landslides. Can Geotech J. 42(5):1437–1448. doi: 10.1139/t05-064.
   Web of Science ®Google Scholar
• Metha RL, Koli SR, Koli VR. 2015. Landslide hazard zonation using remote sensing and GIS technology. A case study of landslide prone area near Mahabaleshwar, Maharashtra. Int J Eng Res Gen Sci. 3(4):6–16.
   Google Scholar
• Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M. 2014. Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci. 46(1):33–57. doi: 10.1007/s11004-013-9511-0.
   Web of Science ®Google Scholar
• Middlemess CS. 1885. A fossiliferous series in the Lower Himalaya in Garhwal. Rec Geol Surv India. 18:73–77.
   Google Scholar
• Mihaela C, Martin B, Marta CJ, Marius V. 2011. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci. 63:397–406.
   Web of Science ®Google Scholar
• Milaghardan AH, Delavar M, Chehreghan A. 2016. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory. Model Earth Syst Environ. 2(4):1–10. doi: 10.1007/s40808-016-0240-5.
   Google Scholar
• Misra RC, Sharma RP. 1967. Geology of Devidhura area Almora UP. J Geol Soc India. 8:110–118.
   Google Scholar
• Montgomery DR, Dietrich WE. 1994. A physically based model for the topographic control on shallow landsliding. Water Resour Res. 30(4):1153–1171. doi: 10.1029/93WR02979.
   Web of Science ®Google Scholar
• Mustafa RM, Pradhan B, Helmi ZMS, Yusoff ZM. 2017. Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos. Geomat Nat Hazards Risk. 8(2):1935–1954. doi: 10.1080/19475705.2017.1401013.
   Web of Science ®Google Scholar
• 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. 2010:901095. doi: 10.1155/2010/901095.
   Web of Science ®Google Scholar
• Norouzi Banis Y, Bathurst JC, Walling DE. 2004. Use of caesium-137 data to evaluate SHETRAN simulated long-term erosion patterns in arable lands. Hydrol Process. 18(10):1795–1809. doi: 10.1002/hyp.1447.
   Web of Science ®Google Scholar
• Nsengiyumva JB, Luo G, Amanambu AC, Mind’je R, Habiyaremye G, Karamage F, Ochege FU, Mupenzi C. 2019. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Sci Total Environ. 659:1457–1472. doi: 10.1016/j.scitotenv.2018.12.248.
   PubMed Web of Science ®Google Scholar
• Numada M, Konagai K, Ito H, Johansson J. 2003. Material point method for run-out analysis of earthquake-induced long-traveling soil flows.
   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. 37(9):1264–1276. doi: 10.1016/j.cageo.2010.10.012.
   Web of Science ®Google Scholar
• 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. 64:180–197. doi: 10.1016/j.jseaes.2012.12.014.
   Web of Science ®Google Scholar
• Pachauri AK. 2010. Landslide hazard mapping and assessment in Himalayas. Fifth Int Conf on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics; May 24–29; San Diego, CA, USA. http:scholarsmine.mst.edu/icrageesd/05Sicrageesd/session04b/22.
   Google Scholar
• Pack RT, Tarboton DG, Goodwin CN. 1998. The SINMAP approach to terrain stability mapping. 8th Congress of the International Association of Engineering Geology; Vancouver, Canada.
   Google Scholar
• Pack RT, Tarboton DG, Goodwin CN. 2001. Assessing terrain stability in a GIS using SINMAP. 15th Annual GIS Conference; Vancouver, British Columbia, Canada.
   Google Scholar
• Pal M, Mather PM. 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Rem Sens Environ. 86(4):554–565. doi: 10.1016/S0034-4257(03)00132-9.
   Web of Science ®Google Scholar
• Paranunzio R, Laio F, Nigrelli G, Chiarle M. 2015. A method to reveal climatic variables triggering slope failures at high elevation. Nat Hazards. 76(2):1039–1061. doi: 10.1007/s11069-014-1532-6.
   Web of Science ®Google Scholar
• Park NW. 2015. Using maximum entropy modeling for landslide susceptibility mapping with multiple geo environmental data sets. Environ Earth Sci. 73(3):937–949. doi: 10.1007/s12665-014-3442-z.
   Web of Science ®Google Scholar
• Park S, Choi C, Kim B, Kim J. 2013. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci. 68(5):1443–1464. doi: 10.1007/s12665-012-1842-5.
   Web of Science ®Google Scholar
• Parkin G. 1995. [SHETRAN water flow component, equations and algorithms] [Ph.D. thesis]. Newcastle upon Tyne: Newcastle University.
   Google Scholar
• Pasuto A, Silvano S. 1998. Rainfall as a trigger of shallow mass movements. A case study in the Dolomites, Italy. Environ Geol. 35(2–3):184–189. doi: 10.1007/s002540050304.
   Google Scholar
• Petley DN, Hearn GJ, Hart A, Rosser NJ, Dunning SA, Oven K, Mitchell WA. 2007. Trends in landslide occurrence in Nepal. Nat Hazards. 43(1):23–44. doi: 10.1007/s11069-006-9100-3.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Dholakia MB, Prakash I, Pham HV. 2016. A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng. 34(6):1807–1824. doi: 10.1007/s10706-016-9990-0.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB. 2015. 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(1–2):255–273. doi: 10.1007/s00704-015-1702-9.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Prakash I, Dholakia MB. 2015. A comparison study of predictive ability of support vector machine and Naive Bayes tree methods in landslide susceptibility assessment at an area between Tehri Garhwal and Pauri Garhwal, Uttarakhand State (India) using GIS. Proceedings of the National Symposium on Geomatics for Digital India Conventions of ISG & ISRS; Jaipur, India.
   Google Scholar
• Polykretis C, Chalkias C, Ferentinou M. 2019. Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bull Eng Geol Environ. 78(2):1173–1187. doi: 10.1007/s10064-017-1125-1.
   Web of Science ®Google Scholar
• Pourghasemi HR, Kerle N. 2016. Random forests and evidential belief function‐based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci. 75:185.
   Web of Science ®Google Scholar
• Pourghasemi HR, Mohammady M, Pradhan B. 2012. 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.
   Web of Science ®Google Scholar
• Pourghasemi HR, Rahmati O. 2018. Prediction of the landslide susceptibility: which algorithm, which precision? Catena. 162:177–192. doi: 10.1016/j.catena.2017.11.022.
   Web of Science ®Google Scholar
• Pradhan AM, Kim Y. 2017. Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul, Korea. Bull Eng Geol Environ. 76(4):1263–1279. doi: 10.1007/s10064-016-0919-x.
   Web of Science ®Google Scholar
• Pradhan AMS, Dawadi A, Kim YT. 2012. Use of different bivariate statistical landslide susceptibility methods: a case study of Khulekhani Watershed, Nepal. J Nepal Geol Soc. 44:1–12. doi: 10.3126/jngs.v44i0.24483.
   Google Scholar
• Pradhan B, Lee S, Buchroithner MF. 2010. A GIS-based backpropagation neural network model and its cross-application and validation for landslide susceptibility analysis. Comput Environ Urban Syst. 34(3):216–235. doi: 10.1016/j.compenvurbsys.2009.12.004.
   Web of Science ®Google Scholar
• Pradhan B, Lee S. 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw. 25(6):747–759. doi: 10.1016/j.envsoft.2009.10.016.
   Web of Science ®Google Scholar
• Pradhan B, Youssef AM. 2010. Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci. 3(3):319–326. doi: 10.1007/s12517-009-0089-2.
   Web of Science ®Google Scholar
• Pradhan B. 2010. 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. doi: 10.1007/s12524-010-0020-z.
   Web of Science ®Google Scholar
• 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. 51:350–365. doi: 10.1016/j.cageo.2012.08.023.
   Web of Science ®Google Scholar
• Rai DK, Xiong D, Zhao W, Zhao D, Zhang B, Dahal NM, Wu Y, Baig MA. 2022. An investigation of landslide susceptibility using logistic regression and statistical index methods in Dailekh District, Nepal. Chin Geogr Sci. 32(5):834–851. doi: 10.1007/s11769-022-1304-2.
   Web of Science ®Google Scholar
• Ram P, Gupta V, Devi M, Vishwakarma N. 2020. Landslide susceptibility mapping using bivariate statistical method for the hilly township of Mussoorie and its surrounding areas, Uttarakhand Himalaya. J Earth Syst Sci. 129(1):1–18.
   Web of Science ®Google Scholar
• Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. 2014. 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(2):725–742. doi: 10.1007/s12517-012-0807-z.
   Web of Science ®Google Scholar
• Richards A, Argles T, Harris N, Parrish R, Ahmad T, Darbyshire F, Draganits E. 2005. Himalayan architecture constrained by isotopic tracers from clastic sediments. Earth Planet Sci Lett. 236(3–4):773–796. doi: 10.1016/j.epsl.2005.05.034.
   Web of Science ®Google Scholar
• Robinson TR, Rosser NJ, Densmore AL, Williams JG, Kincey ME, Benjamin J, Bell HJA. 2017. Rapid post-earthquake modelling of coseismic landslide magnitude and distribution for emergency response decision support. Nat Hazards Earth Syst Sci Discuss. 17:1521–1540. doi: 10.5194/nhess-2017-83.
   Web of Science ®Google Scholar
• Roşian G, Horváth C, Muntean L, Mihăiescu R, Arghiuş V, Maloş C, Baciu N, Măcicăşan V, Mihăiescu T. 2016. Analysing landslides spatial distribution using GIS. Case study: Transylvanian Plain. Proenvironment. 9:366–372.
   Google Scholar
• Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S. 2010. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology. 114(3):129–142. doi: 10.1016/j.geomorph.2009.06.020.
   Web of Science ®Google Scholar
• Rupke J. 1974. Stratigraphic and structural evolution of the Kumaon Lesser Himalaya. Sediment Geol. 11(2–4):81–265. doi: 10.1016/0037-0738(74)90027-X.
   Web of Science ®Google Scholar
• Saha A, Mandal S, Saha S. 2020. Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods. SN Appl Sci. 2(10):1647. doi: 10.1007/s42452-020-03441-3.
   Web of Science ®Google Scholar
• 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(1):61–69. doi: 10.1007/s10346-004-0039-8.
   Web of Science ®Google Scholar
• Salimah A, Hasan R. 2020. Stability analysis and landslide countermeasures using Plaxis2d in the Cigentis Watersheds, Karawang, West Jawa. doi: 10.4108/eai.18-10-2019.2289904.
   Google Scholar
• Sangeeta, Maheshwari BK. 2019. Earthquake-induced landslide hazard assessment of Chamoli District, Uttarakhand using relative frequency ratio method. Indian Geotech J. 49, 108–123. doi: 10.1007/s40098-018-0334-2.
   Web of Science ®Google Scholar
• Sarkar S, Kanungo D. 2004. An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Rem Sens. 70(5):617–625. doi: 10.14358/PERS.70.5.617.
   Web of Science ®Google Scholar
• Sarkar S, Roy AK, Raha P. 2016. Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India. Catena. 142:36–46. doi: 10.1016/j.catena.2016.02.009.
   Web of Science ®Google Scholar
• Searle MP, Simpson RL, Law RD, Parrish RR, Waters DJ. 2003. The structural geometry, metamorphic and magmatic evolution of the Everest massif, High Himalaya of Nepal–south Tibet. J Geol Soc. 160(3):345–366. doi: 10.1144/0016-764902-126.
   Google Scholar
• Sezer EA, Pradhan B, Gokceoglu C. 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Exp Syst Appl. 38(7):8208–8219. doi: 10.1016/j.eswa.2010.12.167.
   Web of Science ®Google Scholar
• Sharma G, Sanjeevi S. 2015. Landslide hazard zonation using remote sensing, ground penetrating radar surveys and geographical information system in Katteri. Int J Curr Eng Technol. 5(2):1160–1169.
   Google Scholar
• Sharma LP, Nilanchal P, Ghose MK, Debnath P. 2013. Synergistic application of fuzzy logic and geo-informatics for landslide vulnerability zonation – a case study in Sikkim Himalayas, India. Appl Geomat. 5(4):271–284. doi: 10.1007/s12518-013-0115-7.
   Google Scholar
• Simon A, Larsen MC, Hupp CR. 1990. The role of soil processes in determining mechanisms of slope failure and hillside development in a humid-tropical forest, Eastern Puerto Rico. Geomorphology. 3(3–4):263–286. doi: 10.1016/0169-555X(90)90007-D.
   Google Scholar
• Solaimani K, Mousav SZ, Kavian A. 2013. Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab J Geosci. 6(7):2557–2569. doi: 10.1007/s12517-012-0526-5.
   Web of Science ®Google Scholar
• Srivastava P, Mitra G. 1994. Thrust geometries and deep structure of the outer and lesser Himalaya, Kumaon and Garhwal (India): implications for evolution of the Himalayan fold and thrust belt. Tectonics. 13(1):89–109. doi: 10.1029/93TC01130.
   Web of Science ®Google Scholar
• Steorts RC. 2014. Linear and quadratic discriminant analysis; p. 1–21.
   Google Scholar
• Sur U, Singh P, Meena SR. 2020. Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomat Nat Hazards Risk. 11(1):2176–2209. doi: 10.1080/19475705.2020.1836038.
   Web of Science ®Google Scholar
• Suzen 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):303–321. doi: 10.1016/S0013-7952(03)00143-1.
   Web of Science ®Google Scholar
• Süzen ML, Kaya B. 2012. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth. 5(4):338–355. doi: 10.1080/17538947.2011.586443.
   Web of Science ®Google Scholar
• Tang Y, Feng F, Guo Z, Feng W, Li Z, Wang J, Sun Q, Ma H, Li Y. 2020. Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: a comparative study from the Loess Plateau Area in Shanxi (China). J Clean Prod. 277:124159. doi: 10.1016/j.jclepro.2020.124159.
   Web of Science ®Google Scholar
• Tanoli JI,Chen N,Regmi A,Jun L. 2017. Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along upper Bhote Koshi, Nepal. Arabian J. Geosci. doi: 10.10.1007/s12517-017-3026-9.
   Web of Science ®Google Scholar
• Tien Bui D, Pradhan B, Lofman O, Revhaug I. 2012. Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naive Bayes models. Math Probl Eng. 2012:974638. doi: 10.1155/2012/974638.
   Web of Science ®Google Scholar
• Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I. 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(2):361–378. doi: 10.1007/s10346-015-0557-6.
   Web of Science ®Google Scholar
• Tiwari VN, Pandey VHR, Kainthola A, Singh PK, Singh KH, Singh TN. 2020. Assessment of Karmi Landslide Zone, Bageshwar, Uttarakhand, India. J Geol Soc India. 96(4):385–393. doi: 10.1007/s12594-020-1567-0.
   Web of Science ®Google Scholar
• 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). Geomorphology. 249:119–136. doi: 10.1016/j.geomorph.2015.06.001.
   Web of Science ®Google Scholar
• Turner AK, Schuster RL. 1996. Landslides: investigation and mitigation. Special Report 247. Washington (DC): Transportation Research Board, The National Academies Press.
   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(9):1101–1114. doi: 10.1016/j.cageo.2010.04.004.
   Web of Science ®Google Scholar
• Vakhshoori V, Zare M. 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomat Nat Hazards Risk. 7(5):1731–1752. doi: 10.1080/19475705.2016.1144655.
   Web of Science ®Google Scholar
• Valdiya KS. 1979. Outline of the structure of the Kumaun Himalaya. J Geol Soc India 20:147–157.
   Web of Science ®Google Scholar
• Valdiya KS. 1980. Geology of Kumaun Lesser Himalaya, Interim Record: Dehradun, Wadia Institute of Himalayan Geology.
   Google Scholar
• Van Westen CJ. 1997. Statistical landslide hazard analysis, ILWIS 2.1 for Windows application guide. Enschede: ITC Publication; p. 73–84.
   Google Scholar
• Varnes DJ, IAEG. 1984. Commission on landslide and other mass movement on slopes, landslide hazard zonation: a review of principles and practice. Paris: The UNESCO Press; p. 63.
   Google Scholar
• Varnes DJ. 1978. Slope movement types and processes. In: Schuster RL, Krizek RJ, editors. Special report 176: landslides: analysis and control. Washington (DC): Transportation and Road Research Board, National Academy of Science; p. 11–33.
   Google Scholar
• Vijith H, Madhu G. 2008. Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol. 55(7):1397–1405. doi: 10.1007/s00254-007-1090-2.
   Google Scholar
• Vorpahl P, Elsenbeer H, Märker M, Schröder B. 2012. How can statistical models help to determine driving factors of landslides? Ecol Model. 239:27–39. doi: 10.1016/j.ecolmodel.2011.12.007.
   Web of Science ®Google Scholar
• Wang Y, Fang Z, Hong H. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993. doi: 10.1016/j.scitotenv.2019.02.263.
   PubMed Web of Science ®Google Scholar
• Watari M. 1986. Mechanism and mitigation measures of slope disasters. Sankaido, Japan Landslide Society; p. 170.
   Google Scholar
• Weidner L, DePrekel K, Oommen T, Vitton S. 2019. Investigating large landslides along a river valley using combined physical, statistical, and hydrologic modeling. Eng Geol. 259:105169. doi: 10.1016/j.enggeo.2019.105169.
   Web of Science ®Google Scholar
• Wilson RC, Torikai JD, Ellen SD. 1992. Development of rainfall warning thresholds for debris flows in the Honolulu District, Oahu. U.S. Geological Survey Open-File Report 92-521; p. 45.
   Google Scholar
• Wu W, Sidle RC. 1997. Application of a distributed shallow landslide analysis model (dSLAM) to managed forested catchments in Oregon, USA.
   Google Scholar
• 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(3):274–287. doi: 10.1016/j.catena.2011.01.014.
   Web of Science ®Google Scholar
• 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):251–266. doi: 10.1016/j.enggeo.2005.02.002.
   Web of Science ®Google Scholar
• Yilmaz I, Keskin I. 2009. GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ. 68(4):459–471. doi: 10.1007/s10064-009-0188-z.
   Web of Science ®Google Scholar
• Yilmaz I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci. 61(4):821–836. doi: 10.1007/s12665-009-0394-9.
   Web of Science ®Google Scholar
• Yin A. 2005. Cenozoic evolution of the Himalayan orogen as constrained by a long-strike variations of structural geometry, exhumation history, and foreland sedimentation. Earth Sci Rev. 76(1–2):1–131. ISSN 0012-8252. doi: 10.1016/j.earscirev.2005.05.004.
   Web of Science ®Google Scholar
• Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al Katheeri MM. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 13(5):839–856. doi: 10.1007/s10346-015-0614-1.
   Web of Science ®Google Scholar
• Zhang T, Han L, Chen W, Shahabi H. 2018. Hybrid integration approach of entropy with logistic regression and support vector machine for landslide susceptibility modelling.
   Google Scholar