Landslide Susceptibility Mapping Using Remote Sensing and Bivariate Frequency Ratio Method in Bahrabise Municipality of Sindhupalchok District, Nepal
DOI:
https://doi.org/10.3126/jist.v30i1.76175Keywords:
Landslide Susceptibility, Frequency Ratio, Bahrabise, Sindhupalchok.Abstract
Sindhupalchok district, located in the northeastern region of Bagmati Province, Nepal, is highly susceptible to landslides. Every year during the monsoon season, many people lose their lives and properties due to landslides. Bahrabise municipality, in particular, has experienced repeated landslide events in recent years, yet remains under–researched in terms of localized susceptibility mapping. The area’s steep terrain, fragile geology, and proximity to settlements make it a critical zone for detailed landslide risk assessment. The present study focuses on mapping areas susceptible to landslides in Bahrabise Municipality, which lies in the Sindhupalchok district of Nepal. An integrated methodology was adopted in this study, incorporating Geographic Information System (GIS), Remote Sensing (RS), and the Frequency Ratio (FR) model. To prepare the landslide susceptibility map for the area, various spatial and environmental datasets were utilized, including precipitation, slope gradient, elevation, proximity to drainage networks, topographic wetness index, geological features, distance from roads, land use and land cover (LULC), terrain curvature, and slope orientation. These factors and thematic layers were derived from both remotely sensed data and ground – based information such as GPS field surveys, visual inspections, and household interviews, and analyzed using ArcGIS software. The Frequency Ratio (FR) model was applied to assign weights to each thematic layer, representing their influence on landslide occurrence. These weighted factors were then combined within the ArcGIS environment to generate the final landslide susceptibility map for the study area. The resulting landslide susceptibility map classified the study area into four zones, with 47.9% falling under high and very high susceptibility. The model achieved a prediction accuracy of 81.3%, indicating strong reliability in identifying landslide-prone areas. The study examines the feasibility of employing more comprehensive methodologies to identify the landslide susceptibility in the region.
Downloads
References
Adhikari, N., Acharya, S., Dhungana, J., Shrestha, S., & Bhuju, D.R. (2015). Climate Change Vulnerability Mapping for Central Development Region of Nepal. Proceedings of IOE Graduate Conference, 2015.
Akgun, A., Dag, S., & Bulut, F. (2007). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Engineering Geology, 54, 1127–1143.
Arrogante-Funes, P., Bruzón, A.G., Arrogante-Funes, F., Ramos-Bernal, R.N., & Vázquez-Jiménez, R. (2021). Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment. International Journal of Environmental Research and Public Health, 18(22), 11987. https://doi.org/10.3390/ijerph182211987.
Ayer, P.B., & Bhandari, B.P. (2024). Landslide Susceptibility Mapping Using Frequency Ratio and Weight of Evidence Models in Purchaudi Municipality, Baitadi District, Nepal. Nepal Journal of Environmental Science, 12(2), 59–72. https://doi.org/10.3126/njes.v12i2.73671.
Beven, K.J., & Kirkby, M.J. (1979). A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834.
Bhandari, B.P., & Dhakal, S. (2021). Decadal Evolution of Landslides in the Siwalik Zone: A Case Study of Babai Watershed, Nepal. Journal of Institute of Science and Technology, 26(1), 57–62. https://doi.org/10.3126/jist.v26i1.37864.
Bhandari, B.P., Dhakal, S., & Tsou, C.-Y. (2024). Assessing the Prediction Accuracy of Frequency Ratio, Weight of Evidence, Shannon Entropy, and Information Value Methods for Landslide Susceptibility in the Siwalik Hills of Nepal. Sustainability, 16(5), 2092. https://doi.org/10.3390/su16052092.
Bhandari, B.P & Dhakal, S. (2018). Lithological Control on Landslide in the Babai Khola Watershed, Siwaliks Zone of Nepal. American Journal of Earth Sciences, 5, 54–64.
Burt, T., & Butcher, D. (1986). Stimulation from simulation? A teaching model of hillslope hydrology for use on microcomputers. Journal of Geography in Higher Education, 10(1), 23–39. https://doi.org/10.1080/03098268608708953.
Carrara, A., Guzzetti, F., Cardinali, M., & Reichenbach, P. (1999). Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard. Natural Hazards, 20(2/3), 117–135. https://doi.org/10.1023/A:1008097111310.
Chauhan, S., Sharma, M., & Arora, M.K. (2010). Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides, 7(4), 411–423. https://doi.org/10.1007/s10346-010-0202-3.
Chen, W., Wang, J., Xie, X., Hong, N., Trung, V. N., Bui, D. T., Wang, G., & Li, X. (2016). Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environmental Earth Sciences, 75(20), 1344.
Chung, C.-J., & Fabri, A. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing, 65(12), 1389–1399.
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S., & Paudyal, P. (2008). Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology, 102(3–4), 496–510. https://doi.org/10.1016/j.geomorph.2008.05.041.
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., & Althuwaynee, O.F. (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. Natural Hazards, 65(1), 135–165. https://doi.org/10.1007/s11069-012-0347-6
Ercanoglu, M., Kasmer, O., & Temiz, N. (2008). Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment, 67(4), 565–578. https://doi.org/10.1007/s10064-008-0170-1.
Erener, A., Mutlu, A., & Sebnem Düzgün, H. (2016). A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Engineering Geology, 203, 45–55. https://doi.org/10.1016/j.enggeo.2015.09.007.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W.Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology, 102(3–4), 85–98. https://doi.org/10.1016/j.enggeo.2008.03.022.
Gautam, P., Kubota, T., & Aditian, A. (2021). Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal. Geoenvironmental Disasters, 8(1), 30. https://doi.org/10.1186/s40677-021-00200-3.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1–4), 272–299.
Hamza, T., & Raghuvanshi, T.K. (2017). GIS based landslide hazard evaluation and zonation – A case from Jeldu District, Central Ethiopia. Journal of King Saud University - Science, 29(2), 151–165. https://doi.org/10.1016/j.jksus.2016.05.002.
Hung, C.-H., & Lo, K.-C. (2015). Relationships between Ambient Ozone Concentration Changes in Southwestern Taiwan and Invasion Tracks of Tropical Typhoons. Advances in Meteorology, 2015, 1–17. https://doi.org/10.1155/2015/402976.
Kavzoglu, T., Sahin, E.K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425–439. https://doi.org/10.1007/s10346-013-0391-7.
Kayastha, P., Dhital, M.R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers & Geosciences, 52, 398–408. https://doi.org/10.1016/j.cageo.2012.11.003.
Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095–1113. https://doi.org/10.1007/s002540100310.
Lee, S., Pradhan, B., & Lee, M.J. (2010). Landslide susceptibility mapping using GIS-based weighted linear combination, and frequency ratio models in the Gangjeong-Goryeong area, Korea. Journal. Journal of Earth Science, 119(6), 681–694. https://doi.org/DOI: 10.1007/s12040-010-0068-6.
Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982–990. https://doi.org/10.1007/s00254-005-1228-z.
Magliulo, P., Di Lisio, A., & Russo, F. (2009). Comparison of GIS-based methodologies for the landslide susceptibility assessment. GeoInformatica, 13(3), 253–265. https://doi.org/10.1007/s10707-008-0063-2.
Mansouri Daneshvar, M.R. (2014). Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides, 11(6), 1079–1091. https://doi.org/10.1007/s10346-013-0458-5.
MoHA. (2019). Nepal Disaster Report 2019. Government of Nepal, Ministry of Home Affairs.
Mondal, S., & Maiti, R. (2013). Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed. International Journal of Disaster Risk Science, 4(4), 200–212.
Moore, I.D., Grayson, R.B., & Ladson, A.R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30. https://doi.org/10.1002/hyp.3360050103.
Pathak, L., & Devkota, K.C. (2022). Landslide susceptibility assessment in the Rangun Khola watershed of far western Nepal. Journal of Nepal Geological Society, 33–44. https://doi.org/10.3126/jngs.v63i01.50827.
Pisano, L., Zumpano, V., Malek, Ž., Rosskopf, C.M., & Parise, M. (2017). Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future. Science of The Total Environment, 601–602, 1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231.
Pokhrel, K., & Bhandari, B.P. (2019). Identification of Potential Landslide Susceptible Area in the Lesser Himalayan Terrain of Nepal. Journal of Geoscience and Environment Protection, 07(11), 24–38. https://doi.org/10.4236/gep.2019.711003.
Poudel, K., & Regmi, A.D. (2016). Landslide susceptibility mapping along Tulsipur-Kapurkot road section and its surrounding region using bivariate statistical model. Journal of Nepal Geological Society, 50, 83–93.
Pradhan, B.K. (2007). Disaster preparedness for natural hazards: Current status in Nepal. International Centre for Integrated Mountain Development (ICIMOD).
Pradhan, U., Shrestha, R., KC, S., & Sharma, S. (2002). Geological map of petroleum exploration block—7, Malangawa, Central Nepal (Scale: 1: 250,000). Petroleum Exploration Promotion Project, Department of Mines and Geology, Kathmandu.
Pyakurel, A., K.C., D., & Dahal, B.K. (2024). Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning. Scientific Reports, 14(1), 5902. https://doi.org/10.1038/s41598-024-54898-w.
Regmi, A.D., Yoshida, K., Pourghasemi, H.R., DhitaL, M.R., & Pradhan, B. (2014). Landslide susceptibility mapping along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. Journal of Mountain Science, 11(5), 1266–1285. https://doi.org/10.1007/s11629-013-2847-6.
Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001.
Reis, S., Yalcin, A., Atasoy, M., Nisanci, R., Bayrak, T., Erduran, M., Sancar, C., & Ekercin, S. (2012). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environmental Earth Science, 66(7), 2063–2073.
Rostami, Z.A., Al-modaresi, S.A., Fathizad, H., & Faramarzi, M. (2016). Landslide susceptibility mapping by using fuzzy logic: A case study of Cham-gardalan catchment, Ilam, Iran. Arabian Journal of Geosciences, 9(17), 685. https://doi.org/10.1007/s12517-016-2720-3.
Searle, M.P., Windley, B.F., Coward, M.P., Cooper, D.J. W., Rex, A.J., Rex, D., Tingdong, L., Xuchang, X., Jan, M.Q., Thakur, V.C., & Kumar, S. (1987). The closing of Tethys and the tectonics of the Himalaya. Geological Society of America Bulletin, 98(6), 678. https://doi.org/10.1130/0016-7606(1987)98<678:TCOTAT>2.0.CO;2.
Thapa, D., & Bhandari, B.P. (2019a). GIS-Based frequency ratio method for identification of potential landslide susceptible area in the Siwalik Zone of Chatara-Barahakshetra section, Nepal. Open Journal of Geology, 09(12), 873–896. https://doi.org/10.4236/ojg.2019.912096.
Tien Bui, D., Tuan, T.A., 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. https://doi.org/10.1007/s10346-015-0557-6.
Tofani, V., Segoni, S., Agostini, A., Catani, F., & Casagli, N. (2013). Technical Note: Use of remote sensing for landslide studies in Europe. Natural Hazards and Earth System Sciences, 13(2), 299–309. https://doi.org/10.5194/nhess-13-299-2013.
Wu, Y., Li, W., Wang, Q., Liu, Q., Yang, D., Xing, M., Pei, Y., & Yan, S. (2016). Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arabian Journal of Geosciences, 9(2), 84. https://doi.org/10.1007/s12517-015-2112-0.
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA, 72(1), 1–12. https://doi.org/10.1016/j.catena.2007.01.003.
Yalcin, A., Reis, S., Aydinoglu, A.C., & 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. https://doi.org/10.1016/j.catena.2011.01.014.
Zhu, A.-X., Wang, R., Qiao, J., Qin, C.-Z., Chen, Y., Liu, J., Du, F., Lin, Y., & Zhu, T. (2014). An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology, 214, 128–138. https://doi.org/10.1016/j.geomorph.2014.02.003.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Institute of Science and Technology, T.U.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The views and interpretations in this journal are those of the author(s). They are not attributable to the Institute of Science and Technology, T.U. and do not imply the expression of any opinion concerning the legal status of any country, territory, city, area of its authorities, or concerning the delimitation of its frontiers of boundaries.
The copyright of the articles is held by the Institute of Science and Technology, T.U.
