doi: 10.2307/2984504ĭeoja B, Dhital M, Thapa B, Wagner A (1991) Mountain risk engineering handbook. Ann Math Stat 28:325–339ĭempster AP (1968) A generalization of Bayesian inference. doi: 10.1007/s002540000163ĭempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Environ Geol 54(2):311–324ĭai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. doi: 10.1007/s1266-7ĭahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. doi: 10.1007/BF02590167ĭahal R (2014) Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya. doi: 10.1007/s0025-6Ĭhung CJ, Fabbri A (2003) Validation of spatial prediction models for landslide hazard mapping. Çevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Int J Appl Earth Obs Geoinf 10(3):374–387 doi: 10.1007/s1105-9Ĭarranza EJM, Van Ruitenbeek F, Hecker C, van der Meijde M, van der Meer FD (2008) Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Ore Geol Rev 22(1):117–132Ĭarranza EJM, Woldai T, Chikambwe EM (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. doi: 10.1007/s1106-0Ĭarranza EJM, Hale M (2003) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Episodes 14(1):52–61īui DT, Pradhan B, Lofman O, Revhaug I, Dick ØB (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Canadian Geotechnical Society, pp 307–324īrabb EE (1991) The world landslide problem. In: Fourth International Symposium on landslides, Toronto, Canada. doi: 10.1186/s4067-yīrabb EE (1984) Innovative approaches to landslide hazard mapping. doi: 10.1007/BF02261716Īnbalagan R, Kumar R, Lakshmanan K, Parida S, Neethu S (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. Comput Geosci 44:120–135Īn P, Moon WM, Bonham-Carter GF (1994) Uncertainty management in integration of exploration data using the belief function. doi: 10.1007/s0025-8Īlthuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. The produced susceptibility map can be useful for general land-use planning, as well as it can provide information to the planners for the adaptation of appropriate mitigating measures for landside hazards in the Patu Khola watershed.Īkgun 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. The verification result showed that the model performed very well with the success rate of 85.13 %, and predictive accuracy is 86.42 %. The map was evaluated using area under the curve (AUC) of success and prediction rate. Thus, from the analysis of the landslide data and factor maps, a susceptibility map of the region was obtained by using the EBF model. Twelve different landslide causative factors were considered for this study. From the mapped landslides, 80 % were randomly selected for developing the model, and the remaining 20 % were used for validating the model. For this, a landslide inventory map consisting of 279 different types of landslides was generated from the published topographic maps, aerial photograph, and Google Earth image interpretations and from the detailed filed survey. In this respect, we have used an evidential belief function (EBF) model in the GIS environment to know different landslide-susceptible zones in the watershed. Thus, it is very essential to develop a landslide hazard map of this basin. The river valley is heavily populated as the growing population has forced the people to dwell in such risky zone. The basin is disrupted by two regional scale thrusts (Bhotechaur Thrust and Kapurkot Thrust) and several imbricate faults making it prone to instabilities. The rocks belonging to the Precambrian Lesser Himalaya and the Miocene Siwalik cover the northern portion of the watershed, while the southern flat area is covered by the Quaternary river terraces. Patu Khola watershed lies in Dang Valley of West Nepal.
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