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Background: Landslide hazard mapping is a fundamental tool for disaster management activities in fragile mountainous terrains. The main purpose of this study is to carry out landslide hazard assessment by weights-of- evidence modelling and prepare optimized mitigation map in the Higher Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the North-West marginal hills of the Achham. Thematic maps representing various factors that are related to landslide activity were generated using field data and GIS techniques. Landslide events of the old landslides were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. Results: The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value. The hazard map prepared with five hazard classes viz. Very high, High, Moderate, Low and Very Low was used to determine the location of prime causative factors responsible for instability. Spatial distribution of causative factor was correlated with the mechanism and scale of failure. For the mitigation of such shallow-seated failure, bioengineering techniques (i.e. grass plantation, shrubs plantation, tree plantation along with small scale civil engineering structures) are taken as cost-effective and sustainable measures for the least developed country like Nepal. Based on prime causitive factors and required bioengineering techniques for stabilization of unstable road side slopes, mitigation map is prepared having 14 classes of mitigation measures. Conclusion: The mitigation map reveled only 6.8% road side slopes require retaining structures however that more than half of the instable slope can be treated with simple vegetative techniques. Therefore, high hazard doensnot demand expensive structures to mitigate it in each every case. Keywords: Bioengineering, GIS, Hazard, Landslide, Mitigation, Rainfall, Weight-of-evidence modeling Background Varnes (1984) defined landslide hazard as the prob- In mountains of Himalayas, landslides are frequent ability of occurrence of a landslide within a specified phenomenon as the mountain building process and in period and within a given area. The landslide hazard interference with human activity they become a prob- zonation is the process of classification of land with lem. Mountain slope failure is mainly provoked by equal landslide hazard value (Varnes 1984) and it pro- combine effect of intrinsic and extrinsic parameters. vides information on the susceptibility of the terrain to The extrinsic events like rainfall and earthquake trig- slope failures. This classified hazard map can be used ger slope. Similarly, intrinsic parameters like bedrock to prepare mitigation plan for the associated hazard. geology, geomorphology, soil depth, soil type, slope Mitigation plan according to the hazard level is very gradient, slope aspect, slope curvature, land use, eleva- useful to optimize linear civil engineering structure like tion, engineering properties of the slope material, land road, which are long and passes through numerous use pattern, drainage pattern and so on have vital roles physical conditions (i.e. optimization in construction, in the landslide occurrence. operation and maintenance). To reduce the Mitigation technique for shallow seated instability, bioengineering * Correspondence: email@example.com techniques are taken as sustainable and cost effective School of Civil Engineering and Mechanics, Huazhong University of Science measures (Deoja et al. 1991; Howell, 1999; Shrestha and Technology, 1037 Luoyu Road, Hongshan District, Wuhan, China 2009; Rai 2010). Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 2 of 9 Study area Landslide hazard The study area is located in the northern hills of the Hazard is a source of risk that may cause damage to, or Achham, Nepal. The study area is within the Higher loss of, life and property. Hazard can also be defined as Himalaya and belongs to the Kalikot and Slyanigad forma- the probability of occurrence of a particularly damaging tion. Kalikot formation has Budhi Ganga gneiss group phenomenon, within a specified period of time and consisting augen gneisses, granetic gneiss and feldspathic within a given area, because of a set of existing or pre- schist and Ghattegad carbonates group consist bluish dicted conditions in the given time and space. The crystalline limestone, calcareous schist and quartz biotite damaging phenomenon becomes a matter of concern schists. Similarly Salynigad formation consist aplite granite, only when it entails a certain degree of damage or loss gneisses augen, gneisses and biotite gneisses. The study to the population or the resources within its influence. area ranges from 980 to 2924 m from mean sea level. In the context of Nepal’s mountain the major hazard is The total watershed is taken as study area for purpose of rainfall-induced landslide (Dahal et al. 2008). hazard mapping which is about of 65.46 km whereas only To determine landslide hazard of any study area in- strip of 100 m either side of road is taken for preparation trinsic (bedrock geology, geomorphology, soil depth, soil of mitigation map. The mean annual precipitation ranges type, slope gradient, slope aspect, slope convexity and from 1486 to 1739 mm. Most slopes face west, and the concavity, elevation, slope forming material, land use slope gradient generally increases with increase in eleva- pattern, drainage pattern, sediment transport and wetness tion. Colluvium is the main slope material above the index) and extrinsic (rainfall, earthquakes, and volcanoes) bedrock. The area is mainly covered with cultivated land. variables are used (Siddle et al. 1991; Wu and Sidle 1995; In 2009, the study area experienced extreme events of Atkinson and Massari 1998; Dai et al. 2001; Çevik and monsoon rainfall and faced 84 landslides. There were 91 Topal 2003; Paudyal and Dhital 2005; Dahal et al. old landslides traced from field survey and Arial Photo- 2008). Since the extrinsic factor is difficult to estimate graph taken at different dates by Department of Survey. instead of landslide hazard, the landslide susceptibility Inventory for both old and new landslides are plotted in mapping is done considering only intrinsic variables GIS (Fig. 1). Because of a number of lakes in the study area, (Dai et al. 2001). A landslide hazard zonation consists currently different governmental and non-governmental of two different aspects (Van Westen et al. 2003): a) agencies have shown interest on the infrastructure devel- The assessment of the susceptibility of the terrain for a opment of the area. Therefore, hazard analysis of the area slope failure and b) The determination of the probabil- is necessary for the sustainability of such infrastructure. ity that a triggering event occurs. Fig. 1 Location of study area along with old and new landslides Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 3 of 9 A region with terrain condition similar to the region this method is comparing the possibility of landslide where landslide has occurred is considered to be suscep- occurrence with observed landslides. tibletolandslides(VanWestenand Terlien1996).Geo- Based on field survey various causative factors were graphic Information Systems (GIS) with capability of identified, including slope, slope aspect, geology, flow handling and integrating multiple intrinsic variables in rela- accumulation, relief, landuse, soil type, soil depth, distance tion to the spatial distribution of landslides has gained the to road, curvature, wetness index, sediment transport success in landslide hazard mapping (Dahal et al. 2008). index and mean annual rainfall (Fig. 2). These thematic map were prepared by using topographic maps and aerial Methods photographs taken by the Department of Survey, Govern- Hazard map ment of Nepal. Field surveys were carried out to prepare Weights-of-evidence modelling used to prepare landside landslide inventory, soil type, soil depth and landuse maps. hazard map (Dahal et al. 2008) is based on Bayesian During survey landslides were plotted to the topographic probability model. This model was first developed and map of 1:50,000. Positions of landslide in map was deter- used for mineral potential assessment (Bonham-Carter mined by GPS. Meanwhile soil type and landuse were also 2002). This method aided with GIS was very popular in delineated in same topographic map. Whereas depth of the field of mineral potential mapping (Emmanuel et al. soil is estimated by the help of open-cut, terraces and 2000; Tangestani and Moore 2001). Zahiri et al. (2006) landslides. A landslide distribution map before and after used weights-of-evidence modelling for mapping of cliff the extreme monsoon rainfall events in 2009 were pre- instabilities associated with mine subsidence. This pared after field survey (Fig. 2). These thematic data layer method has also been applied to landslide susceptibility were prepared using the GIS software ILWIS 3.3. mapping (Lee et al. 2002; Van Westen et al. 2003, Lee In this study the thirteen intrinsic variables and one and Choi 2004, Lee et al. 2007; Neuhäuser and Terhorst extrinsic variable was used for hazard analysis. All factor 2007; Sharma and Kumar 2008). Dahal et al. 2008 used maps with cell size of 10 m × 10 m were stored in raster this method for landslide hazard mapping. The method format. Each factor map was crossed with landslide in- calculates the weight for each landslide causative factor ventory map and weight map was prepared with the help based on the presence or absence of the landslides of series of commands written in script. Mathematical within the area. The related mathematical relationships expression used to calculate positive and negative weight are described below. are as follows: (Bonham-Carter 2002) and can be expressed as () below: N þN þ 1 2 W ¼ log ð3Þ i e N þN 3 4 PFfg jL W ¼ log ð1Þ () i e PFjL N þN − 1 2 W ¼ Log ð4Þ i e N N þN 3 4 Similarly, negative weights of evidence, W , as follows: Where N , N , N and N are No of cell units repre- 1 2 3 4 P F jL senting the presence of landslides and potential landslide W ¼ log ð2Þ i e P F jL predictive factor, presence of landslides and absent of potential landslide predictive factor, absence of landsides Where, L is the presence of a landslide, F is presence and presence of potential landslide predictive factor and of a causative factor, F is the absence of causative factor absence of both landslides and potential landslide pre- and L is absence of landslide. dictive factor respectively. A positive weight ( W ) indicates that the causative i Landslide Hazard Index (LHI) map was prepared by factor is present at the landslide location, and the mag- numerically adding the resultant weighted factor map nitude of this weight is an indication of the positive obtained by assigning weights of the classes of each correlation between presence of the causative factor thematic layer: and landslides. A negative weight ( W ) indicates an absence of the causative factor and shows the level of negative correlation. LHI ¼ W Slope þ W Aspect þ W Disdrn þ W Curv f f f f þW Disrd þ W FA þ W Geo þ W Soilt f f f f Data preparation þW Landu þ W Relief þ W Soild þ W STI f f f f The main step for landslide hazard mapping is data col- þW WetI þ W Rain: f f lection and preparation of a spatial database from which ð5Þ relevant factors can be extracted. The main feature of Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 4 of 9 Fig. 2 Thematic maps Three attribute maps of new, old and all landslides 87.56 and 92.61% of total landslides respectively. Fig. 4 were prepared from LHI values (Fig. 3), which were in provides percentage coverage of landslides in various the range from −23.1 to 12.77. The ability of LHI to higher rank percentage of LHI. predict landslide occurrences was verified using the The prediction rate when LHI map of old landslides success rate curve (Chung and Fabbri 2003), prediction crossed with new landslides is similar to the success rate, and effect analysis (Van Westen et al. 2003; Lee rates as above. It is independent, and when all maps and Choi 2004; Dahal et al. 2006). The success rate in- were combined for the LHI calculation, it gave 78.24% dicates what percentage of all landslides occurs in the prediction accuracy for the new landslides (Fig. 5). classes with the highest value of susceptibility. When More than 72% of the new landslides were well covered old landslides are used for LHI calculation and new by 30% of the high value of LHI calculated from the landslides are used for prediction, the calculated accur- old landslides. acy rate is called prediction rate (Van Westen et al. 2003; For providing classified hazard maps, reference to Lee et al. 2007) and is the most suitable parameter for prediction rate curves (see Fig. 5) was made and five independent validation of LHI. landslide hazard classes were defined: very low (<25% The success rate curves of all three maps are shown in class of low to high LHI value), low (25–60% class of Fig. 4. These curves are the measures of goodness of fit. low to high LHI value), moderate (60–75% class of low In the case of new landslides, the success rate reveals to high LHI value), high (75–90% class of low to high that 10% of the study area where LHI had a higher rank LHI value), and very high (>90% class of low to high could explain 68.66% of total new landslides. Likewise, LHI value, i.e., most higher LHI values) were estab- 30% of higher LHI value could explain 95.07% of all lished. Hazard map of overall watershed was prepared landslides. Similarly, for the cases of old landslides and first and area within road corridor was clipped for miti- all landslides, 30% high LHI value could explain about gation optimization (Fig. 6). Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 5 of 9 Fig. 3 Landslide Hazard Index map Results and discussion to drain, soil depth, soil type, aspect and slope in de- From the classified hazard map of the road corridor scending order (Table 1). (Fig. 6), each pixel of high hazard and very high hazard Jovani (2015) carried out study on national scale land- class has been crossed with all intrinsic factors weight slide hazard assessment along the road corridors of two map and top three were sorted out. From the study, it Caribbean islands, the study only gave the cost of landslide was found that among 13 factor maps, landuse has the clearance and repair of damage rather than mitigation. It highest contribution to the LHI value and then distance is clear that damaged caused by rainfall induced disaster in 2010 to the highways is 5% of GDP of the Saint Lucia. Fig. 4 Success rate curves of landslide hazard values calculated from Fig. 5 Prediction rate curves of landslide hazard values calculated three types of landslide inventory maps from the inventory map of the old landslides Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 6 of 9 Fig. 6 Landslide hazard zonation map: a Overall watershed and b Timilsen-Ramaroshan Road corridor Anbalagan et al. (2008) prepared a meso-scale land- ditches, slope flattening, benching, anchoring etc. slide hazard zonation mapping and suggested that which are either expensive or not suitable for the area planner should avoid the high hazard area or take having high relative relief like Himalayas. precautionary measures during implementation. These This paper is focused on low cost mitigation measures researches are basically either for planning or for re- for rural infrastructures. Bioengineering techniques, use of pair and maintenance. Still there is very few literature living plants in conjunction with small scale civil engineer- about the use of hazard map for mitigation aspect. ing structures, are taken as the low cost mitigation tech- Siddan and Veerappan (2014) prepared hazard zon- niques. These techniques are taken as cost-effective and ation map for a highway section. They have proposed sustainable measures for the least developed country like some general mitigation measures like concrete Nepal and are very useful for mitigation of shallow-seated Table 1 Effect analysis of the factor map Factor map/Class % presence in top three w Land use; Barren land 24 Distance to drain; 20–50 m, 50–100 m and >200 m 19 Soil depth; Shallow 19 Soil type; Colluvium 17 Aspect; S-W 9 Slope; Steep 8 Other 4 Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 7 of 9 Table 2 Cost comparison of conventional and bioengineering failure. Rai (2010), conducted comparative study and mitigation works (Rai 2010) concluded that cost of conventional civil engineering Item Unit Quantity Cost (NRs.) techniques is double to the cost of bioengineering tech- niques for stabilizing same landslide site (Table 2). Cost of Bioengineering works 5,875,704.00 Besides low construction and maintenance cost it has Slide clearance m 390 26,910.00 many socio-economic and environmental benefits. Construction of plum concrete wall m 1350 4,872,150.00 Bioengineering techniques (i.e. grass plantation, shrubs Construction of gabion wall m 120 157,920.00 plantation, tree plantation along with small scale civil Construction of dry wall m 107 95,444.00 engineering structures) for mitigation of shallow seated Rill and ridge formation m 85 15,045.00 instability problem depends on the characteristics of fail- ure (Howell 1999; Deoja et al. 1991). Mechanism of fail- Slope trimming m 1807 38,140.00 ure is depend on presence of different intrinsic factor Backfilling m 896 103,040.00 and its classes. Considering the fact that every class has Installation of sub-soil drain m 180 177,480.00 some distinct characteristics and mechanism of failure, Coir netting m 877 156,106.00 therefore mitigation measure is proposed to overcome Grass plantation m 1893 132,510.00 the effect of each class on slope stability (Table 3). Brushlayering m 581 18,011.00 Mitigation map Grass seeds broadcasting on slope m 3380 64,220.00 Classified hazard map was statistically analysed to find Shrub seeds sowing on slope m 948 10,428.00 out the most predominating factors causing landslide. Fruit plantation no 150 600.00 The analysis of each cell unit of hazard map shows that Bamboo plantation no 50 7700.00 there are altogether twelve classes or combination of dif- Cost of Civil Engineering Works 12,201,833.00 ferent classes responsible for instability. These classes in- Earth work in excavation m 2581 296,815.00 clude eight predominating classes of different factor maps whereas, three are combination of two classes and Earth work in backfilling m 7350 845,250.00 a combination of three classes. Plum Concrete revetment wall (1:2:4) m 1350 4,832,100.00 The mitigation measures proposed based on different Gabion wall m 2123 2,793,868.00 predominating class is overlapped in each and every cell PCC (1:2:4) m 280 1,079,400.00 units. As the result, the concise mitigation representa- Cement masonry cut drain in (1:4) Rm. 200 727,000.00 tion of study area is presented in matrix form (Table 4). Cement masonry surface drain (1:4) Rm. 120 469,800.00 Mitigation map of the study area was prepared after conducting analysis in ILWIS and EXCEL. Low cost miti- Cement masonry chute (1:4) Rm. 100 643,100.00 gation raster map of Timilsen-Ramaroshan District Road Grass Plantation m 7350 514,500.00 was prepared (Fig. 7) by clipping mitigation map of study (1USD = NRS 98.17 on 09 Oct 2010, Source: Nepal Rastra Bank) area and road corridor map. Mitigation map depicts that overall mitigation structure can be classified in fourteen classes derived from seven basic structure types. Table 3 Mitigation measures per class and combination Class Problems Mitigation Code S-W aspect Erosion Vegetation A Barren land Erosion Vegetation A Loose colluvium Erosion Retaining wall, vegetation B Shallow soil depth Slips Vegetation A Steep slope Slips Retaining wall, benching G Distance to Drain 20–50 m Scour, Drainage Toe protection, surface and sub-surface drain C Distance to Drain 50–100 m Drainage Surface and sub-surface drain D Distance to Drain >200 m Drainage Surface drain E Combination of 3,4 Erosion Retaining wall, vegetation B Combination of 3,5 Erosion, flow Retaining wall, benching and vegetation F Combination of 4,5 Slips Retaining wall, benching G Combination of 3,4,5 Erosion, flow Retaining wall, benching and vegetation F Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 8 of 9 Table 4 Mitigation matrix Class S-W Barren Distance to Drain 20–50 50–100 >200 m None A A C D E Loose colluvium B B BC BD BE Shallow soil depth A A AC AD AE Steep slope GA GA GC GD GE Combination of 3, 4 B B BC BD BE Combination of 3, 5 F F FC FD FE Combination of 4, 5 GA GA GC GD GE Combination of 3, 4, 5 F F FC FD FE Fig. 8 Distribution of mitigation measures by type required for stabilization The mitigation map of the road corridor clearly depict that 60% of road side slope is naturally stable and doesn’t required mitigation works. The remaining 40% slope is required different mitigation measures (Fig. 8). high hazard zone but not site specific. In this context, The area required vegetation for stabilization is found to this study will fill the existing gap for the use of hazard be 20.4%, similarly 6.8% of the road side slope required zonation for site specific mitigation mapping. From the retaining wall and 16% of road side slope required drai- prepared mitigation map, the conclusions are drawn as ninge facility. Since the terrain is steep with high relative follows: relief and the slope will be steeper after construction, slope flattening and benching necessary for 16% of un- The first and the most important conclusion of this stable roadside slope. research is, mitigation measure for slope stability is more realistic and sustainable only after considering Conclusions landslide hazard index as well as the causative Landslide hazard mapping is essential in delineating factors. The mitigation map of the study area landslide prone areas and optimizing low cost mitiga- revealed that only 6.8% road side slopes required tion measures in mountainous regions. Amongst vari- retaining structures. Therefore, high hazard always ous techniques, this study applied weights-of-evidence doesn’t demand expensive structures to stabilize it. modelling for landslide hazard analysis, to the northern More often, they are stabilized by very simple mountain in the Higher Himalaya of Achham, Nepal. measure as per its mechanism and causing factor There are very few literatures available for mitigation of instability. mapping by using hazard zonation. Some authors has More than half of (20.4% out of 40% area) the general recommendation of mitigation measures for the instable area can be stabilized with simple bioengineering techniques like grass and shrubs plantation and remaining half will be stabilized in conjunction with small scale civil engineering structures. Therefore, the mitigation approach is much more cost effective in terms of construction cost (Rai 2010) in addition to the social and environmental benefits. These techniques are functionally sound on stabilizing the shallow seated landslides which is the major problem in Himalayan region during construction and operation of roads. The concept of mitigation matrix prepared in this research is new concept and is very useful to deal with classified mitigation hazard map for Nepalese mountain slopes. The optimized mitigation measures might reduce the blockade time of road and improve life standard of the people living in remote villages of Achham Fig. 7 Mitigation measures for Timilsen-Ramaroshan Road and Kalikot districts. Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 9 of 9 Abbreviations Neuhäuser, B., and G. Terhorst. 2007. Landslide susceptibility assessment using GDP: Gross Domestic Product; GIS: Geological Information System; “weights-ofevidence” applied to a study area at the Jurassic escarpment LHI: Landslide Hazard Index (SW-Germany). Geomorphology 86: 12–24. Paudyal, P., and M.R. Dhital. 2005. Landslide hazard and risk zonation of Thankot– Chalnakhel area, central Nepal. Journal of Nepal Geological Society 31: 43–50. Acknowledgement Rai, S. 2010. A case study: conventional engineering & bioengineering approach for Authors would like to acknowledge local people of the study area for slope stabilisation on rural roads. Nepal: District Road Support Programme. providing assistance, help and co-operation during field data collection. Sharma, M., and R. Kumar. 2008. GIS-based landslide hazard zonation: a case We would like to thanks Mr. Chitra Thapa and Mr. Diwakar K C for their study from the Parwanoo area, Lesser and Outer Himalaya, H P, India. valuable help and advice during this research. Bulletin of Engineering Geology and the Environment 67: 129–137. Shrestha, H.R. 2009. Harmonizing Rural Road Development with Mountain Environment: Authors’ contributions Green Roads in Nepal. Nepal: TCDPAP&FIDIC/ASPAC International Conference. BKD carried out data collection, conducted analysis and drafted manuscript. Siddan, A., and R. Veerappan. 2014. Landslide hazard zonation mapping in Ghat RKD has prepared research design, monitored outcome and reviewed road section of Kolli Hills, India. Journal of Mountain Science 11(5): 1308–1325. manuscript. Both authors read and approved the final manuscript. Siddle, H.J., D.B. Jones, and H.R. Payne. 1991. Development of a methodology for landslip potential mapping in the Rhondda Valley. In Slope Stability Engineering Thomas Telford, London, ed. R.J. Chandler, 137–142. Competing interests Tangestani, M.H., and F. Moore. 2001. Porphyry copper potential mapping using The authors declare that they have no competing interests. the weights-of-evidence model in a GIS, northern Shahr-e-Babak, Iran. Australian Journal of Earth Science 48: 695–701. Author details Van Westen, C.J., and T.J. Terlien. 1996. An approach towards deterministic School of Civil Engineering and Mechanics, Huazhong University of Science landslide hazard analysis in GIS. A case study from Manizales (Colombia). and Technology, 1037 Luoyu Road, Hongshan District, Wuhan, China. Earth Surface Process and Landforms 21: 853–868. Geodisaster Research Center, Central Department of Geology, Tribhuvan Van Westen, C.J., N. Rengers, and R. Soeters. 2003. Use of geomorphological University, Kritipur, Kathmandu, Nepal. information in indirect landslide susceptibility assessment. Natural Hazards 30: 399–419. Received: 17 September 2016 Accepted: 28 January 2017 Varnes, D.J. 1984. Landslide hazard zonation: a review of principles and practice. In Commission on landslides of the IAEG, UNESCO, Natural Hazards No 3, 61. Wu, W., and R.C. Sidle. 1995. A distributed slope stability model for steep forested References basins. Water Resource Research 31: 2097–2110. Anbalagan, R., D. Chakraborty, and A. Kohli. 2008. Landslide hazard zonation Zahiri, H., D.R. Palamara, P. Flentje, G.M. Brassington, and E. Baafi. 2006. A GIS- mapping on meso-scale for systematic town planning in mountainous based weights-of-evidence model for mapping cliff instabilities associated terrain. Journal of Scientific & Industrial Research 67: 486–497. with mine subsidence. Environmental Geology 51: 377–386. Atkinson, P.M., and R. Massari. 1998. Generalized linear modelling of landslide susceptibility in the Central Apennines, Italy. Computers & Geosciences 24: 373–385. Bonham-Carter, G.F. 2002. Geographic information systems for geoscientist: Modelling with GIS. In Computer Methods in the Geosciences 13 Pergamon, ed. D.F. Merriam, 302–334. New York: Elsevier. Çevik, E., and T. Topal. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology 44: 949–962. Chung, C.-J.F., and A.G. Fabbri. 2003. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30: 451–472. Dahal, R. K., Hasegawa, S., Yamanaka, M., and K. Nishino. 2006. Rainfall triggered flow-like landslides: understanding from southern hills of Kathmandu, Nepal and northern Shikoku, Japan. Proc 10th Int Congr of IAEG, The Geological Society of London, IAEG2006, Paper number (819): 1–14. Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, S. Dhakal, and P. Paudyal. 2008. Predictive modeling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102: 496–510. http://iaeg2006.geolsoc.org.uk/cd/PAPERS/IAEG_819.PDF. Dai, F.C., C.F. Lee, J. Li, and Z.W. Xu. 2001. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology 40: 381–391. Deoja, B., M. Dhital, B. Thapa, and A. Wagner. 1991. Mountain Risk Engineering-Part I, 188–192. Kathmandu: International Center for Integrated Mountain Development. Emmanuel, J., M. Carranza, and Martin Hale. 2000. Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Submit your manuscript to a Natural Resources Research 9: 237–253. journal and beneﬁ t from: Howell, J. 1999. Roadside Bioengineering Reference Manual,81–102. Department of Roads, Government of Nepal. 7 Convenient online submission Jovani, Y. 2015. National Scale Landslide hazard assessment along the road corridors of 7 Rigorous peer review Dominica and Saint Lucia. The Netherlands: University of Twente. Master thesis. Lee, S., and J. Choi. 2004. Landslide susceptibility mapping using GIS and 7 Immediate publication on acceptance the weights-of-evidence model. International Journal of Geogrgaphical 7 Open access: articles freely available online Information Science 18: 789–814. 7 High visibility within the ﬁ eld Lee, S., J. Choi, and K. Min. 2002. Landslide susceptibility analysis and verification 7 Retaining the copyright to your article using the Bayesian probability model. Environmental Geology 43: 120–131. Lee, S., J. Ryu, and I. Kim. 2007. Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression and artificial neural network models: Submit your next manuscript at 7 springeropen.com case study of Youngin, Korea. Landslides 4: 327–338.
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