Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal
Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.
P. Kayastha, F. Smedt, & M. Dhital. (2010). GIS based landslide susceptibility assessment in Nepal Himalaya: A comparison of heuristic and statistical bivariate analysis.
F. Guzzetti, P. Reichenbach, M. Cardinali, M. Galli, & F. Ardizzone. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1–4), 272–299. DOI: 10.1016/j.geomorph.2005.06.002.
A. K. Saha, R. P. Gupta, I. Sarkar, M. K. Arora, & E. Csaplovics. (2005). An approach for GIS-based statistical landslide susceptibility zonation-with a case study in the Himalayas. In: Landslides, 2(1), 61–69. DOI: 10.1007/s10346-004-0039-8.
F. Guzzetti, M. Galli, P. Reichenbach, F. Ardizzone, & M. Cardinali. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Nat. Hazards Earth Syst. Sci., 6(1), 115–131. DOI: 10.5194/nhess-6-115-2006.
S. Sarkar & D. Kanungo. (2006). GIS based landslide susceptibility mapping—A case study in Indian Himalaya. Proc. Interpraevent Int. …, pp. 617–624.
L. J. Wang, M. Guo, K. Sawada, J. Lin, & J. Zhang. (2015). Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena, 135, 271–282. DOI: 10.1016/j.catena.2015.08.007.
F. Guzzetti, A. Carrara, M. Cardinali, & P. Reichenbach. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. In: Geomorphology, 31(1–4), 181–216. DOI: 10.1016/S0169-555X(99)00078-1.
S. D. Pardeshi, S. E. Autade, & S. S. Pardeshi. (2013). Landslide hazard assessment: Recent trends and techniques. SpringerPlus. DOI: 10.1186/2193-1801-2-523.
E. A. Castellanos Abella & C. J. Van Westen. (2006). Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba. Geomorphology. DOI: 10.1016/j.geomorph.2006.10.038.
M. Ercanoglu, C. Gokceoglu, & T. W. J. Van Asch. (2004). Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Nat. Hazards, 32(1), 1–23. DOI: 10.1023/B:NHAZ.0000026786.85589.4a.
H. J. Oh, S. Lee, & G. M. Soedradjat. (2010). Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environ. Earth Sci. DOI: 10.1007/s12665-009-0272-5.
K. Solaimani, S. Z. Mousavi, & A. Kavian. (2013). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab. J. Geosci. DOI: 10.1007/s12517-012-0526-5.
S. Lee & B. Pradhan. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. In: Landslides. DOI: 10.1007/s10346-006-0047-y.
T. H. Mezughi, J. M. Akhir, A. G. Rafek, & I. Abdullah. (2011). Landslide susceptibility assessment using frequency ratio model applied to an area along the E-W highway (Gerik-Jeli). Am. J. Environ. Sci., 7(1), 43–50. DOI: 10.3844/ajessp.2011.43.50.
S. Lee. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int. J. Remote Sens. DOI: 10.1080/01431160412331331012.
Desinventar. (2016). Country profile of Nepal. Available at: https://www.desinventar.net/.
MoHA. (2020). Nepal disaster risk reduction portal. Available at: http://drrportal.gov.np/.
V. Vakhshoori & M. Zare. (2016). Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Nat. Hazards Risk. DOI: 10.1080/19475705.2016.1144655.
A. D. Regmi et al. (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. DOI: 10.1007/s12517-012-0807-z.
H. Shahabi, S. Khezri, B. Bin Ahmad, & M. Hashim. (2014). Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena, 115, 55–70. DOI: 10.1016/j.catena.2013.11.014.
C. J. F. Chung & A. G. Fabbri. (2003). Validation of spatial prediction models for landslide hazard mapping. Nat. Hazards. DOI: 10.1023/B:NHAZ.0000007172.62651.2b.
F. C. Dai, C. F. Lee, J. Li, & Z. W. Xu. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ. Geol., 40(3), 381–391. DOI: 10.1007/s002540000163.
S. Lee & J. A. Talib. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environ. Geol. DOI: 10.1007/s00254-005-1228-z.
A. Yalcin, S. Reis, A. C. Aydinoglu, & T. Yomralioglu. (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. DOI: 10.1016/j.catena.2011.01.014.
S. Reis et al. (2012). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ. Earth Sci. DOI: 10.1007/s12665-011-1432-y.
S. Mondal & R. Maiti. (2013). Integrating the Analytical Hierarchy Process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. Int. J. Disaster Risk Sci. DOI: 10.1007/s13753-013-0021-y.
S. Ghosh et al. (2012). Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Eng. Geol. DOI: 10.1016/j.enggeo.2011.03.016.
D. M. Cruden & R. Fell. (1997). Landslide risk assessment. Available at: https://webapps.unitn.it/Biblioteca/it/Web/EngibankFile/3178275.pdf.
S. Park, C. Choi, B. Kim, & J. Kim. (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. DOI: 10.1007/s12665-012-1842-5.
R. Nagarajan, A. Roy, R. Vinod Kumar, A. Mukherjee, & M. V. Khire. (2000). Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull. Eng. Geol. Environ. DOI: 10.1007/s100649900032.
J. Choi, H. J. Oh, H. J. Lee, C. Lee, & S. Lee. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng. Geol. DOI: 10.1016/j.enggeo.2011.09.011.
Copyright (c) 2021 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.