Prediction Capability of Bivariate Statistical Model for the Evaluation of Landslide Probability in Sub-humid and Seismic Active Region of Azad Kashmir, Lesser Himalayas
Abstract
Planning and management are necessary tools throughout the world, especially in a mountainous regions where various natural hazards affect the area socially and economically. Landslides are one of the most common natural hazards in mountainous regions throughout the world. For this purpose, qualitative and quantitative methods and landslide susceptibility mapping were used to reduce the probability of landslide occurrence in an affected area and landslide mitigation. The study was focused on landslide susceptibility mapping in Neelum valley using a relative effect model integrated with 17 causative factors of landslides. These factors such as elevation, slope gradient, slope aspect, general curvature, geology, plan curvature, profile curvature, drainage density, stream power index, distance from stream, distance from road, topographic roughness index, Normalized difference wetness index, distance from fault, rainfall, landuse landcover, and Normalized difference vegetative index were used for analysis. Neelum valley is the part of the Himalayan region that is often experiencing landslide hazards frequently. Among other methods, it is a statistical method prepared within the Geographical information system environment to develop landslide hazard zones in Neelum valley. The landslide inventory map was shown the presence of past landslides in the study area by using Google Earth and remote sensing imageries. Total landslides were 368 in number, 30% (110) for testing purposes and 70% (258) for training purposes. The validation of Relative effect model was calculated with the Area under the curve such as the success rate curve and the prediction rate curve. This was adopted to check the validation and optimum landslide susceptibility zone categorization. The success rate curve of the model was 82.15% calculated whereas 82.73% was the predictive rate curve. According to the study, landslide susceptibility mapping was classified into four classes with 18.14% of the area being very high zone, 34.04% of the area being high zone, 30.26% area being moderately susceptible zone and 17.56% of the area being low susceptible to landslide occurrence zone. Hence, the results of this study highlight the spatial information of the area that may face landslide hazards and may be very helpful to planners, engineers, government agencies, stakeholders and other participants for the prevention, mitigation, management, and monitoring of landslide hazards and this model may also be applicable in other landslide areas.
References
S. Das, S. Sarkar, and D. Prasanna, “GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya”, Environmental Monitoring and Assessment, vol. 194, no. 3, pp. 1-28, 2022.
R.M. Tayyib, M. Basharat, N. Hameed, M. Shafique, and J. Luo, “A data-driven approach to landslide-susceptibility mapping in mountainous terrain: a case study from the Northwest Himalayas, Pakistan”, Natural Hazards Review, vol.19, no.4, 05018007, 2018.
García-Rodríguez, M. José, J.A. Malpica, B. Benito, and M. Díaz, “Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression”, Geomorphology, vol.95,
pp.172–191, 2008.
L. Bopche, P.P. Rege, and R.D. Joshi, “Landslide susceptibility mapping: an integrated approach using knowledge-based numerical rating scheme, remote sensing, and multiple overlay analysis”, Journal of Applied Remote Sensing, vol.16, no.1, pp. 014503, 2022.
M. Yawen, “Regional Scale Multi-Hazard Susceptibility Assessment: a case study in Mtskheta-Mtianeti, Georgia”, University of Twente Faculty of Geo-Information and Earth Observation (ITC). 2011.
J. Pankaj, and C.J. van Westen, “Use of quantitative landslide hazard and risk information for local disaster risk reduction along a transportation corridor: a case study from Nilgiri district, India”, Natural hazards, vol.65, no.1, pp. 887-913, 2013.
R.P. Gupta and B.C. Joshi, “Landslide hazard zoning using the GIS approach-a case study from the Ramganga catchment, Himalayas”, Engineering Geology, vol. 28, no.1-2, pp. 119-131, 1990.
T.H. Tran, N.D. Dam, F.E. Jalal, and N. Al-Ansari “GIS-based soft computing models for landslide susceptibility mapping: A case study of Pithoragarh district, Uttarakhand state, India”, Mathematical Problems in Engineering, 2021.
U. Kamp, L.A. Owen, B.J. Growley, and G.A. Khattak, “Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps”, Natural hazards, vol. 54, no. 1, pp. 1-25, 2010.
U. Sur, P. Singh, and S.R. Meena, “Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data”, Geomatics, Natural Hazards, and Risk, vol. 11, no.1, pp. 2176-2209, 2020.
D.P. Kanungo, M.K. Arora, R.P. Gupta, and S. Sarkar, “Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas”, Landslides, vol. 5, no. 4, pp. 407-416, 2008.
F.C. Dai, C.F. Lee and Y.Y. Ngai, “Landslide risk assessment and management: an overview”, Engineering Geology, vol.64, no.1, pp.65-87, 2002.
Asian Development Bank and World Bank (ADB-WB), “Preliminary damage and needs assessment”, Asian development bank and world bank, Islamabad, Pakistan, 124, 2005.
W. Chen, W. Li, E. Hou, Z. Zhao, N. Deng, H. Bai, and D. Wang, “Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China”, Arabian Journal of Geosciences, vol. 7, no. 11, pp. 4499-4511, 2014.
D.P. Kanungo, M.K. Arora, S. Sarkar, and R.P. Gupta, “Landslide susceptibility zonation (LSZ) mapping – a review’, Journal of South Asia Disaster Studies, vol. 2, no. 1, pp. 81-105, 2009.
M. Conforti, S. Pascale, G. Robustelli, and F. Sdao, “Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)”, Catena, vol.113, pp. 236-250, 2014.
S. Chakraborty, and R. Pradhan, “Development of GIS-based landslide information system for the region of East Sikkim”, International Journal of Computer Applications, vol. 49, no. 7, 2012.
A.N. Khan, A.E. Collins, and F. Qazi,” Causes and extent of environmental impacts of landslide hazard in the Himalayan region: a case study of Murree, Pakistan”, Natural Hazards, vol. 57, no. 2, pp. 413-434, 2011.
A.N. Khan, and A.U. Rahman “Landslide hazards in the mountainous region of Pakistan”, Pakistan Journal of Geography, vol. 16, pp. 38-51, 2006.
J. Choi, H.J. Oh, H.J. Lee, C. Lee, and S. Lee, “Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS”, Engineering Geology, vol. 124, pp. 12-23, 2012.
A. Pandey, P.P. Dabral, V.M. Chowdary, and N.K. Yadav, “Landslide hazard zonation using remote sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India”, Environmental Geology, vol. 54, no. 7, pp. 1517-1529, 2008.
D. Meghanadh, V.K. Maurya, A. Tiwari, and R. Dwivedi, “A multi-criteria landslide susceptibility mapping using deep multi-layer perceptron network: A case study of Srinagar-Rudraprayag region (India)”, Advances in Space Research, vol. 69, no. 4, pp.1883-1893, 2022.
A.S. Dhakal, T. Amada, and M. Aniya, “Landslide hazard mapping and the application of GIS in the Kulekhani watershed, Nepal”, Mountain Research and Development, pp. 3-16, 1999.
D.J. Varnes, “Landslide hazard zonation: a review of principles and practice (No. 3)”, Natural Hazards, UNESCO, Paris. 1984.
AGS, “Guidelines for landslide susceptibility, hazard and risk zoning for land use planning”, Australian Geomechanics. vol. 42, pp. 13–36, 2007.
M.G. Anderson, and E. Holcombe, “Community-based landslide risk reduction-managing disasters in small steps. Library of Congress Cataloging-in-Publication Data”, The World Bank, Washington, DC, 2013.
R. Fell, J. Corominas, C. Bonnard, L. Cascini, E. Leroi, and W. Z. Savage, “Guidelines for landslide susceptibility, hazard and risk zoning for land use planning”, Engineering Geology, vol. 102, no. 3-4, pp. 85-98, 2008.
M. Shafique, M. van der Meijde, and M.A. Khan, “A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing perspective”, Journal of Asian Earth Sciences, vol. 118, pp. 68-80, 2016.
Y.He, and R.E. Beighley, “GIS‐based regional landslide susceptibility mapping: a case study in southern California”, Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, vol. 33, no.3, pp. 380-393, 2008.
A. Nandi, and A. Shakoor, “A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses”, Engineering Geology, vol. 110, no. 1-2, pp. 11-20, 2010.
Y. Bahrami, H. Hassani, and A. Maghsoudi, “Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran”, GeoJournal, vol. 86, pp. 1797–1816, 2020.
P. Kayastha, and M.R. Dhital, F. De-Smedt, “Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal”, Computer Geosciences. Vol. 52, pp. 398–408, 2013.
M. Komac, “A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia”, Geomorphology, vol. 74, no. 1-4, pp. 17-28, 2006.
A. Yalcin, S. Reis, A. C. Aydinoglu, and T. Yomralioglu, “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, vol. 85, no. 3, pp. 274-287, 2011.
M.A. Aziz, A.H. Khan, M. Adnan, and H. Ullah, “Traditional uses of medicinal plants used by Indigenous communities for veterinary practices at Bajaur Agency, Pakistan”, Journal of ethnobiology and ethnomedicine, vol. 14, no. 1, pp. 1-18, 2018.
A. Mahmood, R.N. Malik, Z.K. Shinwari, and A. Mahmood, “Ethnobotanical survey of plants from Neelum, Azad Jammu and Kashmir, Pakistan”, Pakistan Journal of Botany, vol.43, no.10, pp. 10, 2011.
K.S. Ahmad, H. Mansoor, F. Sana, A. Muhammad, A. Farooq, N. Mehwish, Z. Aneela, and A. Iftikhar, “Tribe Andropogoneae from Neelum Valley, Azad Jammu, and Kashmir: Phylogeny Based On Morpho-Anatomy”, Pakistan Journal of Botany, vol. 49, pp. 73-82, 2017.
O. Hungr, S. Leroueil, and L. Picarelli, “The Varnes classification of landslide types, an update”, Landslides, vol. 11, no. 2, pp. 167-194, 2014.
S. Mandal, and K. Mandal, "Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya, India.", Spatial Information Research, vol. 26.1, pp. 59-75, 2018.
R, Anbalagan, “Landslide hazard evaluation and zonation mapping in mountainous terrain”, Engineering Geology, vol. 32, no. 4, pp. 269-277, 1992.
M. Kannan, E. Saranathan, and R. Anabalagan, “Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India”, Arabian Journal of Geosciences, vol. 6, no. 8, pp. 2901-2913, 2013.
M. Ercanoglu, and C. Gokceoglu, “Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)”, Engineering Geology, vol. 75, no. 3-4, pp. 229-250, 2004.
M. Kouli, C. Loupasakis, P. Soupios, and F. Vallianatos, “Landslide hazard zonation in high-risk areas of Rethymno Prefecture, Crete Island, Greece”, Natural hazards, vol. 52, no. 3, pp. 599-621, 2010.
Mika, “Weathering of Igneous Rocks” 2013. [Online]. Available: http://wwwgeomikacom/blog/2013/08/17/weathering-igneous/.
A.M. Youssef, M. Al-Kathery, and B. Pradhan, “Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models”, Geosciences Journal, vol. 19, no. 1, pp. 113-134, 2015.
D. Tien Bui, B. Pradhan, I. Revhaug, and C. T. Tran, “A comparative assessment between the application of fuzzy unordered rules induction algorithm and J48 decision tree models in spatial prediction of shallow landslides at Lang Son City, Vietnam”, Remote Sensing Applications in Environmental Research, pp. 87-111, 2014.
Q. Wang, W. Li, Y. Wu, Y. Pei, M. Xing, and D. Yang “A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models”, Journal of Earth System Science, vol. 125, no. 3, pp. 645-662, 2016.
L. Ayalew, and H. Yamagishi, “The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan”, Geomorphology, vol. 65, no. 1-2, pp. 15-31, 2005.
D.T. Bui, B. Pradhan, O. Lofman, I. Revhaug, and O.B. Dick, “Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks”, Geomorphology, vol. 171, pp. 12-29, 2012.
M. Ercanoglu, and C. Gokceoglu, “Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach”, Environmental Geology, vol. 41, no.6, pp. 720-730, 2002.
M.S. Alkhasawneh, U.K. Ngah, L.T. Tay, N.A. M. Isa and M. S. Al-batah, “Determination of important topographic factors for landslide mapping analysis using MLP network”, The Scientific World Journal, vol. 2013, pp. 1-12, 2013.
H.J. Oh, and B. Pradhan, “Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area”, Computers & Geosciences, vol. 37, no. 9, pp. 1264-1276, 2011.
S. Singh, “Geomorphology. Allahabad: Prayag Pustak Bhawan,” 2000.
S.J. Riley, S.D. DeGloria, and R. Elliot, “Index that quantifies topographic heterogeneity”, Intermountain Journal of sciences, vol. 5, no. 1-4, pp. 23-27, 1999.
I.D Moore, and J.P. Wilson, “Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation”, Journal of soil and water conservation, vol. 47, no.5, pp. 423-428, 1992.
H.A. Nefeslioglu, T.Y. Duman, and S. Durmaz, “Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)”, Geomorphology, vol. 94, no. 3-4,
pp. 401-418, 2008.
S. Himan, K. Saeed, A. Bahrain, and H. Mazlan, “Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio, and logisticregression models”, Catena, vol. 115, pp. 55–70, 2014.
C.W. Chen, H. Saito, and T. Oguchi, “Rainfall intensity–duration conditions for mass movements in Taiwan”, Progress in Earth and Planetary Science, vol. 2, no. 1, pp. 1-13, 2015.
P. Biswajeet, and L. Saro, “Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model”, Earth Sciences. Frontiers. vol. 14, no. 6. pp. 143–152, 2007.
D. Tien Bui, Q.P. Nguyen, N.D. Hoang, and H. Klempe, “A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS”, Landslides, vol. 14, no. 1, pp. 1-17, 2017.
L. Lundgren, “Studies of soil and vegetation development on fresh landslide scars in the Mgeta Valley, Western Uluguru Mountains, Tanzania”, Geografiska Annaler: Series A, Physical Geography, vol. 60, no. 3-4, pp. 91-127, 1978.
T. Chen, R. Niu, and X. Jia, “A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS”, Environmental Earth Sciences, vol. 75, no. 10, pp. 867, 2016.
A.M. Eker, M. Dikmen, S. Cambazoğlu, S.H.B. Düzgün, and H. Akgün, “Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey”, International Journal of Geographical Information Science, vol. 29,
no. 1, pp. 132-158, 2015.
C. Guo, D.R. Montgomery, Y. Zhang, K. Wang, and Z. Yang, “Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China”, Geomorphology, vol. 248, pp. 93-110, 2015.
H. Hong, C. Xu, I. Revhaug, and D. T. Bui, “Spatial prediction of landslide hazard at the Yihuang area (China): a comparative study on the predictive ability of backpropagation multi-layer perceptron neural networks and radial basic function neural networks”, In Cartography-maps connecting the world, pp. 175-188, 2015.
S.K. McFeeters, “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features”, International journal of remote sensing, vol. 17, no. 7, pp. 1425-1432, 1996.
A. Yalcin, “GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations”, Catena, vol. 72, pp. 1–12, 2008.
D. Myronidis, C. Papageorgiou, and S. Theophanous, “Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)”, Natural Hazards, vol. 81, no. 1, pp. 245–63, 2016.
https://www.arcgis.com/apps/instant/media/index.html
S.M. Fatemi Aghda, V. Bagheri, and M. Razifard, “Landslide susceptibility mapping using the fuzzy logic system and its influences on mainlines in lashgarak region, Tehran, Iran”, Geotechnical and Geological Engineering, vol. 36(2), pp. 915-937, 2018.
M. Razifard, G. Shoaei, and M. Zare, “Application of fuzzy logic in the preparation of hazard maps of landslides triggered by the twin Ahar-Varzeghan earthquakes (2012)”. Bulletin of Engineering Geology and the Environment, vol. 78(1), pp. 223-245, 2019.
R.H Malik, M. Schouppe, D. Fontan, J. Verkaeren, G. Martinotti, K. H. Shaukat Ahmed, and S. Quresh, “Geology of Neelum valley, district Muzaffarabad, Azad Kashmir, Pakistan”, Geological Bulletin, University of Peshawar. vol. 29, pp. 91-111, 1996.
A.M.S Pradhan, and Y.T. Kim, “Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-RI Creek, South Korea”, Natural hazards, vol. 72, no. 2, pp. 1189-1217, 2014.
V. Ramesh, and S. Anbazhagan, “Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect, and fuzzy logic models”, Environmental Earth Sciences, vol. 73, no. 12, pp. 8009-8021, 2015.
D.W. Hosmer Jr., S. Lemeshow, and R.X. Sturdivant, “Model-building strategies and methods for logistic regression. In: Applied logistic regression, 3rd edn. Wiley, Hoboken, pp 89–151, 2000.
S. Lee, “Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data”, International Journal of Remote Sensing, vol.26, no.7, pp. 1477-1491, 2005.
