Landslide Inventorization and Susceptibility Mapping in the Semi-Arid Kargil-Ladakh Region of Northwestern Himalaya
DOI:
https://doi.org/10.17491/jgsi/2024/173894Keywords:
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Landslides often result in damage to property and loss of life in the Himalayan Region because of high seismicity and rugged terrain. To address this issue, the current study focused on developing a landslide inventory based susceptibility map for Kargil-Ladakh Region of North-Western Himalaya. The landslide susceptibility map for the region was generated with the help of Frequency Ratio (FR) Method, landslide inventory layer and eleven influencing factors such as; elevation, geomorphology, aspect, slope, plan curvature, lithology, rainfall, profile curvature, distance from road and lineaments, temperature, and land cover. The results reveal that approximately (50%) of the study-area lies under moderate to very high susceptibility zones. The low and very low susceptibility zones cover the remaining (50%) of the study-area. The findings highlight that several factors significantly contribute to landslide occurrences in the region, namely slope, road network, elevation, rainfall, and land cover. The northern part of the study area is particularly susceptible to landslides due to the presence of the national highway, a high concentration of settlements, and increased infrastructure development. Furthermore, areas surrounding local highways and community road networks show moderate landslide susceptibility. The results were validated with the help of the Receiver Operating Characteristic (ROC) curve, yielding a value of (82%) which is well within the acceptable limit. The results underscore the importance of developing site-specific landslide mitigation strategies to safeguard vulnerable communities in this strategically significant border region of India.
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Achour, Y. and Pourghasemi, H.R. (2019) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci. Front., https://doi.org/10.1016/j.gsf.2019.10.001
Akbar, M., Bhat, M.S., Chanda, A. Lone, F.A. and Thoker, I.A., (2022) Integrating Traditional Knowledge with GIS for Snow Avalanche Susceptibility Mapping in Kargil-Ladakh Region of Trans-Himalayan India. Spat. Inf. Res. doi:10.1007/s41324-022-00471-4
Akbar, M., Amir, A.A. and Bhat, M.S. (2022) Localized mechanism a way forward approach: A case study of covid -19 pandemic in Ladakh, India. Disaster Advan., v.15(4), pp.41–49. https://doi.org/10.25303/1504da041049
Akbar, M., Bhat, M.S. and Khan, A.A. (2023) Multi-hazard susceptibility mapping for disaster risk reduction in Kargil-Ladakh Region of Trans-Himalayan India. Environ. Earth Sci., v.82, pp.68, https://doi.org/10.1007/s12665-022-10729-7
Ahmed, M.F., Rogers, J.D. and Ismail, E.H. (2014) A regional level preliminary landslide susceptibility study of the upper Indus river basin. European Jour. Rem. Sens., v.47(1), pp.343–373. https://doi.org/10.5721/ eujrs20144721
Akgun, A., Kincal, C. and Pradhan, B. (2011) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ. Monit. Assess., v.184(9), pp.5453–5470. https://doi.org/10.1007/s10661-011-2352-8
Al-Najjar, H.A.H. and Pradhan, B. (2020) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci. Front. https://doi.org/10.1016/j.gsf.2020.09.002
Ashish Pandey, A.E. (2007) Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India. Environ. Geol.,
Aykut Akgun, N.T. (2009) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ. Earth Sci., v.61(3), pp.595-611.
Bell, R. and Glade, T. (2004) Quantitative risk analysis for landslides Examples from Bíldudalur, NW-Iceland. Natural Hazards, Earth Syst. Sci., v.4(1), pp.117–131. https://doi.org/10.5194/nhess-4-117-2004
Bhat, M.S., Akbar, M., Falahati, F., Chanda, A. and Khan, A.A. (2022). Flash Flood Susceptibility Mapping Using Drainage Morphometric Parameters in Leh-Ladakh, India. Internat. Res. Jour. Managmt. Sci. Tech., v.13(5), pp.117–131. https://doi.org/10.32804/IRJMST
Bhat, M.S. and Rather, J.A., (2018) Impact of climate change on spring season in the north-western Himalayas: a study of Kashmir valley, India (1901-2000), International Jour. Advan. Res. Sci. Tech., v.7(4).
Bhat M.S., Rather, J.A., Kanth, T.A. and Bhat, M.S., (2015) Core-Winter Temperature in Kashmir Valley (1950-2010) as an Indicator of Climatic Change. Asian Resonance v.iv(iii).
Binh Thai Pham, E.A. (2019) Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms. Sustanability, v.11(16)
Budha, P.B., Paudyal, K. and Ghimire, M. (2016) Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal. Jour. Nepal Geol. Soc., v.50(1), pp.125–131. https://doi.org/10.3126/jngs.v50i1.22872
Chao Zhou, E.A. (2018) Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers and Geosci.
Chen, W., Pourghasemi, H. R., Kornejady, A. and Zhang, N. (2017) Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma, v.305, pp.314–327. https://doi.org/10.1016/j.geoderma.2017.06.020
Dang Quag Thanh, E.A. (2019) GIS based frequency ratio method for landslide susceptability mapping at Da Lat City, Lam Dong Provience, Vietnam. Vietnam Jour. Earth Sci., v.42(1), pp.55-66.
Daniel E.J. and Hobley, H.D. (2012) Reconstruction of a major storm event from its geomorphic signature: the Ladakh floods, 6 August 2010. Geology, v.40(6), pp.483-486
Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S. and Alamri, A. M. (2020). Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review. Appl. Sci., v.10(7), 2466. MDPI AG. https://doi.org/10.3390/app10072466
Dilip Kumar, N.L. (2018) Study and Prediction of Landslide in Uttarkashi, Uttarakhand, India Using GIS and ANN. Amer. Jour. Neural Networks and Applications, v.3(6), pp.63-74.
Dortch, J.M., Owen, L.A., Haneberg, W.C., Caffee, M.W., Dietsch, C. and Kamp, U. (2009) Nature and timing of large landslides in the Himalaya and Trans Himalaya of northern India. Quaternary Sci. Rev., v.28(11-12), pp.1037–1054. https://doi.org/10.1016/j.quascirev.2008.05.002
District Disaster Management Plan Kargil (2017-2018) Office of the district magistrate, chairman, district disaster management authority Kargil.
Froude, M.J. and Petley, D.N. (2018) Global fatal landslide occurrence from 2004 to 2016. Natural Hazards, Earth Syst, Sci., v.18(8), pp.2161–2181. https://doi.org/10.5194/nhess-18-2161-2018
Hobley, D.E.J., Sinclair, H.D. and Mudd, S.M. (2012) Reconstruction of a major storm event from its geomorphic signature: The Ladakh floods, 6 August 2010. Geology, v.40(6), pp.483–486. https://doi.org/10.1130/g32935.1
Hodgkins, S. (n.d.) Mass movement events in the Himalaya: The impact of landslides on Ladakh, India. Geology for Global Development.
Hussain, G., Singh, Y. and Bhat, G.M. (2018) Landslide Susceptibility Mapping along the National Highway-1D, between Kargil and Lamayuru, Ladakh Region, Jammu and Kashmir. Jour. Geol. Soc. India, v.91(4), pp.457–466. https://doi.org/10.1007/s12594-018-0879-9
Hyun-Joo Oh, B.P. (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Elsevier.
Kalantar, B., Pradhan, B., Naghibi, S. A., Motevalli, A. and Mansor, S. (2017) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, v.9(1), pp.49-69. https://doi.org/10.1080/19475705.2017.1407368
Karra, K.E. (2021) Global land use/land cover with Sentinel-2 and deep learning. Internat. Geosci. Rem. Sens. Symp.
Kumar, R. and Anbalagan, R. (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Jour. Geol. Soc. India, v.87(3), pp.271–286. https://doi.org/10.1007/s12594-016-0395-8
Laila Fayez, E.A. (2018) Application of Frequency Ratio Model for the Development of Landslide Susceptibility Mapping at Part of Uttarakhand State, India. Internat. Jour. Appl. Eng. Res., v.13(9), pp.6846-6854.
Lee, S., Hong, S.-M. and Jung, H.-S. (2017) A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea. Sustainability, v.9(1), 48p. MDPI AG. https://doi.org/10.3390/su9010048
Li, Y. and Chen, W. (2019) Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques. Water, v.12(1), pp.113. https://doi.org/10.3390/w12010113
Lorentz, J.F., Calijuri, M.L., Marques, E.G. and Baptista, A.C. (2016) Multicriteria analysis applied to landslide susceptibility mapping. Natural Hazards, v.83(1), pp.41–52. https://doi.org/10.1007/s11069-016-2300-6
Marjanoviæ, M., Kovaèeviæ, M., Bajat, B. and Vo•enílek, V. (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng. Geol., v.123(3), pp.225–234. https://doi.org/10.1016/j.enggeo.2011.09.006
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., Thai Pham, B., Bui, D.T. and Abderrahmane, B. (2020) Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci. Rev., 103225. https://doi.org/10.1016/j.earscirev.2020.103225
Mohan, A., Singh, A.K., Kumar, B. and Dwivedi, R. (2020) Review on remote sensing methods for landslide detection using machine and deep learning. Trans. Emerging Telecommun. Tech. https://doi.org/10.1002/ett.3998
Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C. and Jaedicke, C. (2006) Global landslide and avalanche hotspots. Landslides, v.3(2), pp.159–173. https://doi.org/10.1007/s10346-006-0036-1
Nanda, A.M., Lone, F.A., Ahmed, P. and Kanth, T.A. (2020) Rainfall-induced landslide movements using linear regression analysis along national highway 1D (Jammu and Kashmir, India). Model. Earth Syst. Environ., v.7(3), pp.1863–1875. https://doi.org/10.1007/s40808-020-00908-5
Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Clague, J.J., et al. (2020) Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. Internat. Jour. Environ. Res. Public Health, v.17(14), 4933. MDPI AG. https://doi.org/10.3390/ijerph17144933
Paryani, S., Neshat, A., Javadi, S., and Pradhan, B. (2020) Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping. Natural Hazards. https://doi.org/10.1007/s11069-020-04067-9
Pham, Shirzadi, Shahabi, Omidvar, Singh, Sahana, … Lee. (2019) Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms. Sustainability, v.11(16), 4386. doi:10.3390/su11164386
Pradhan, B., Mansor, S., Pirasteh, S. and Buchroithner, M. F. (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Internat. Jour. Rem. Sens., v.32(14), pp.4075–4087. https://doi.org/10.1080/01431161.2010.484433
Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaie, F., & Blaschke, T. (2019) Multi-hazard probability assessment and mapping in Iran. Sci. Total Environ., https://doi.org/10.1016/j.scitotenv.2019.07.203
Stastical handbook. Kargil (2018–19) District statistical and evaluation officer kargil. https://kargil.nic.in/document/statistical-handbook-for-the-year-2019-20/. Accessed on 20 March 2022
Serkan Kiranyaz, et al. (2020) 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing.
Shit, P.K., Bhunia, G.S. and Maiti, R. (2016) Potential landslide susceptibility mapping using weighted overlay model (WOM). Model. Earth Syst. Environ., v.2(1). https://doi.org/10.1007/s40808-016-0078-x
Shafique, M. (2020) Spatial and temporal evolution of co-seismic landslides after the 2005 Kashmir earthquake. Geomorphology, 107228. https://doi.org/10.1016/j.geomorph.2020.107228
Shafiq, M.U., Islam, Z.U., Abida, A.W., Bhat, M.S., Ahmed, P. Recent trends in precipitation regime of Kashmir valley, India. Disaster Adv., v.12, pp.1–11.
Shafiq, M.U., Bhat, M.S., Rasool, R., Ahmed, P., Singh, H. and Hassan, H (2016) Variability of Precipitation regime in Ladakh region of India from 1901–2000. Jour. Climatol. Weather Forecast, v.4, pp.165. https://doi.org/10.4172/2332-2594.1000165
Swetha, T.V. and Gopinath, G. (2020) Landslides susceptibility assessment by analytical network process: a case study for Kuttiyadi river basin (Western Ghats, southern India). SN Appl. Sci, v.2(11). https://doi.org/10.1007/s42452-020-03574-5
Tilloy, A., Malamud, B. D., winter, H. and Joly-Laugel, A. (2019). A review of quantification methodologies for multi-hazard interrelationships. Earth-Sci. Rev., 102881. https://doi.org/10.1016/j.earscirev.2019.102881
Totschnig, R. and Fuchs, S. (2013) Mountain torrents: Quantifying vulnerability and assessing uncertainties. Eng. Geol., v.155, pp.31–44. https://doi.org/10.1016/j.enggeo.2012.12.019
Wang, H., Zhang, L., Yin, K., Luo, H. and Li, J. (2020) Landslide identification using machine learning. Geosci. Front., doi:10.1016/j.gsf.2020.02.012
Yilmaz, I. (2009) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ. Earth Sci., v.61(4), pp.821–836. https://doi.org/10.1007/s12665-009-0394-9
Yu, C. and Chen, J. (2020) Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM. Symmetry, v.12(6), 1047. MDPI AG. https://doi.org/10.3390/sym12061047
Zare, M., Pourghasemi, H. R., Vafakhah, M. and Pradhan, B. (2012) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab. Jour. Geosci., v.6(8), pp.2873–2888. https://doi.org/10.1007/s12517-012-0610-x