Landslide susceptibility mapping for hazard management along Pakistan’s Balakot-Naran Route
DOI:
https://doi.org/10.15243/jdmlm.2025.123.7401Keywords:
Balakot Valley , frequency ratio , landslide, landslide susceptibility , PakistanAbstract
Landslides represent a significant hazard worldwide, leading to substantial loss of life and property. In Pakistan’s Balakot Valley, severe landslides frequently disrupt key roadways, particularly along the Balakot-Naran route, which serves as a crucial artery for tourism—the region’s primary economic activity. This study aimed to create a high-resolution landslide susceptibility map for a 457 km² area along this road, where landslides are driven by factors such as intense rainfall, weak geological formations, seismic activity, and slope destabilization due to road construction. Using Geographic Information Systems (GIS) and remote sensing, we analyzed nine critical landslide-predictive factors: slope, aspect, lithology, land cover/land use, plan curvature, profile curvature, and proximity to faults, roads, and streams. Our methodology applied the frequency ratio (FR) model to 80% of the landslide inventory data for model construction, while 20% of the inventory was reserved for validation. The resulting susceptibility map, classified into low, moderate, high, and very high-risk zones using the natural breaks method in ArcGIS, indicates that slope gradient, lithology, land cover, and stream proximity are the primary contributors to landslide occurrence in this area. Model performance metrics demonstrate high predictive accuracy, with a success rate of 0.93 and a prediction rate of 0.96. The generated susceptibility map provides a robust tool for hazard management, offering valuable insights for targeted mitigation strategies and sustainable infrastructure development in this landslide-prone region. This study advanced landslide susceptibility mapping by integrating high-impact geospatial factors with an innovative, validated FR approach, supporting safer, data-driven land use planning and disaster preparedness.
References
Aleotti, P. and Chowdhury, R. 1999. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment 58(1):21-44. https://doi.org/10.1007/s100640050066
Aslam, B., Zafar, A. and Khalil, U. 2022. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan. Environment, Development and Sustainability 1-28. https://doi.org/10.1007/s10668-022-02314-6
Ayalew, L. and Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1-2):15-31. https://doi.org/10.1016/j.geomorph.2004.06.010
Bacha, A.S., Shafique, M. and van der Werff, H. 2018. Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan. Journal of Mountain Science 15(6):1354-1370. https://doi.org/10.1007/s11629-017-4697-0
Baig, M.S. and Lawrence, R.D. 1987. Precambrian to early Paleozoic orogenesis in the Himalaya. Kashmir Journal of Geology 5:1-22.
Basharat, M., Rohn, J., Baig, M.S. and Khan, M.R. 2014. Spatial distribution analysis of mass movements triggered by the 2005 Kashmir earthquake in the Northeast Himalayas of Pakistan. Geomorphology 206:203-214. https://doi.org/10.1016/j.geomorph.2013.09.025
Calkins, J.A., Offield, TW., Abdullah, S.K.M. and Ali, S.T. 1975. Geology of the Southern Himalaya in Hazara, Pakistan and Adjacent Areas. US Geological Survet 716. https://doi.org/10.3133/pp716C
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V. and Reichenbach, P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 16(5):427-445. https://doi.org/10.1002/esp.3290160505
Chalkias, C., Ferentinou, M. and Polykretis, C. 2014. GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4(3):176-190. https://doi.org/10.3390/geosciences4030176
Cui, Y., Cheng, D., Choi, C.E., Jin, W., Lei, Y. and Kargel, J.S. 2019. The cost of rapid and haphazard urbanization: lessons learned from the Freetown landslide disaster. Landslides 16(6):1167-1176. https://doi.org/10.1007/s10346-019-01167-x
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S. and 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
Dai, F.C. and Lee, C.F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3-4):213-228. https://doi.org/10.1016/S0169-555X(01)00087-3
Dai, F.C., Lee, C.F. and Ngai, Y.Y., 2002. Landslide risk assessment and management: an overview. Engineering Geology 64(1):65-87. https://doi.org/10.1016/S0013-7952(01)00093-X
Donati, L. and Turrini, M.C. 2002. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology 63(3-4):277-289. https://doi.org/10.1016/S0013-7952(01)00087-4
Feizizadeh, B. and Blaschke, T. 2011. Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. Journal of Earth Science and Engineering 1(1):66-77.
Froude, M.J. and Petley, D.N. 2018. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18(8):2161-2181. https://doi.org/10.5194/nhess-18-2161-2018
García-Rodríguez, M.J., Malpica, J.A., Benito, B. and Díaz, M. 2008. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3-4):172-191. https://doi.org/10.1016/j.geomorph.2007.06.001
Guzzetti, F., Carrara, A., Cardinali, M. and Reichenbach, P. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1-4):181-216. https://doi.org/10.1016/S0169-555X(99)00078-1
Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M. and Chang, K.T. 2012. Landslide inventory maps: new tools for an old problem. Earth-Science Reviews 112(1-2):42-66. https://doi.org/10.1016/j.earscirev.2012.02.001
Hong, H., Chen, W., Xu, C., Youssef, A.M., Pradhan, B. and Bui, D.T. 2016. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto International 32(2):139-154. https://doi.org/10.1080/10106049.2015.1130086
Kamp, U., Growley, B.J., Khattak, G.A. and Owen, L.A. 2008. GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101(4):631-642. https://doi.org/10.1016/j.geomorph.2008.03.003
Kamp, U., Owen, L.A., Growley, B.J. and Khattak, G.A. 2010. Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps. Natural Hazards 54(1):1-25. https://doi.org/10.1007/s11069-009-9451-7
Khan, H., Shafique, M., Khan, M.A., Bacha, M.A., Shah, S.U. and Calligaris, C. 2019. Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Science 22(1):11-24. https://doi.org/10.1016/j.ejrs.2018.03.004
Khan, S.F., Kamp, U. and Owen, L.A. 2013. Documenting five years of land sliding after the 2005 Kashmir earthquake, using repeat photography. Geomorphology 197:45-55. https://doi.org/10.1016/j.geomorph.2013.04.033
Khattak, G.A., Owen, L.A., Kamp, U. and Harp, E.L. 2010. Evolution of earthquake-triggered landslides in the Kashmir Himalaya, northern Pakistan. Geomorphology 115(1-2):102-108. https://doi.org/10.1016/j.geomorph.2009.09.035
Kirschbaum, D., Stanley, T. and Zhou, Y. 2015. Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4-15. https://doi.org/10.1016/j.geomorph.2015.03.016
Lee, S.A.R.O., 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing 26(7):1477-1491. https://doi.org/10.1080/01431160412331331012
Mittal, S.K., Singh, M., Kapur, P., Sharma, B.K. and Shamshi, M.A. 2008. Design and development of instrumentation for landslide monitoring and issue. Journal of Scientific and Industrial Research 67(5):361-365.
Mondal, S. and Maiti, R. 2013. Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. International Journal of Disaster Risk Science 4(4):200-212. https://doi.org/10.1007/s13753-013-0021-y
Nandi, A. and Shakoor, A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 110(1-2):11-20. https://doi.org/10.1016/j.enggeo.2009.10.001
Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology 97(3-4):171-191. https://doi.org/10.1016/j.enggeo.2008.01.004
Qin, M., Cui, P., Jiang, Y., Guo, J. Zhang, G. and Ramzan, M. 2022. Occurrence of shallow landslides triggered by increased hydraulic conductivity due to tree roots. Landslides 19:2593-2604. https://doi.org/10.1007/s10346-022-01921-8
Ramzan, M., Ullah, A. and Ahmad, T. 2023. Sedimentological attributes of the Middle Jurassic Samana Suk Formation exposed in Village Rani-Wah Haripur, Southern Hazara Basin (NW Himalayan Fold and Thrust Belt, Pakistan). Lithology and Mineral Resources 58:501-519. https://doi.org/10.1134/S0024490223700190
Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T. and Akgun, A. 2013. Application of frequency ratio, statistical index, and weights-of evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geoscience 7:725-742. https://doi.org/10.1007/s12517-012-0807-z
Regmi, N.R., Giardino, J.R. and Vitek, J.D. 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115(1-2):172-187. https://doi.org/10.1016/j.geomorph.2009.10.002
Saba, S.B., van der Meijde, M. and van der Werff, H. 2010. Spatiotemporal landslide detection for the 2005 Kashmir earthquake region. Geomorphology 124(1-2):17-25. https://doi.org/10.1016/j.geomorph.2010.07.026
Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F. and Rossi, M. 2017. Characterization and quantification of path dependency in landslide susceptibility. Geomorphology 292:16-24. https://doi.org/10.1016/j.geomorph.2017.04.039
Schicker, R. and Moon, V. 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161:40-57. https://doi.org/10.1016/j.geomorph.2012.03.036
Shahabi, H., Hashim, M. and Ahmad, B.B. 2015. Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environmental Earth Sciences 73(12):8647-8668. https://doi.org/10.1007/s12665-015-4028-0
Shahabi, H., Khezri, S., Ahmad, B.B. and Hashim, M. 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. https://doi.org/10.1016/j.catena.2013.11.014
Shahri, A.A., Spross, J., Johansson, F. and Larsson, S. 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena 183:104-225. https://doi.org/10.1016/j.catena.2019.104225
Shano, L., Raghuvanshi, T.K. and Meten, M. 2021. Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia. Arabian Journal of Geosciences 14(7):1-18. https://doi.org/10.1007/s12517-021-06995-7
Solaimani, K., Mousavi, S.Z. and Kavian, A. 2012. Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geoscience 6:2557-2569. https://doi.org/10.1007/s12517-012-0526-5
Steger, S., Bell, R., Petschko, H. and Glade, T. 2015. Evaluating the effect of modelling methods and landslide inventories used for statistical susceptibility modelling. Engineering Geology for Society and Territory 2:201-204. https://doi.org/10.1007/978-3-319-09057-3_27
Sujatha, E.R., Kumaravel, P. and Rajamanickam, G.V. 2014. Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bulletin of Engineering Geology and the Environment 73(1):147-161. https://doi.org/10.1007/s10064-013-0537-9
Sun, X., Chen, J., Bao, Y., Han, X., Zhan, J. and Peng, W. 2018. Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS International Journal of Geo-Information 7(11):438. https://doi.org/10.3390/ijgi7110438
Umar, Z., Pradhan, B., Ahmad, A., Jebur, M.N. and Tehrany, M.S. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124-135. https://doi.org/10.1016/j.catena.2014.02.005
Vijith, H., Krishnakumar, K.N., Pradeep, G.S., Ninu Krishnan, M.V. and Madhu, G. 2014. Shallow landslide initiation susceptibility mapping by GIS-based weights-of-evidence analysis of multi-class spatial data-sets: a case study from the natural sloping terrain of Western Ghats, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 8(1):48-62. https://doi.org/10.1080/17499518.2013.843437
Wang, Q., Li, W., Xing, M., Wu, Y., Pei, Y., Yang, D. and Bai, H., 2016. Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models. Geosciences Journal 20(5):705-718. https://doi.org/10.1007/s12303-016-0003-3
Weirich, F. and Blesius, L. 2007. Comparison of satellite and air photo-based landslide susceptibility maps. Geomorphology 87(4):352-364. https://doi.org/10.1016/j.geomorph.2006.10.003
Xu, J., Wang, Z., Shen, F., Ouyang, C. and Tu, Y. 2016. Natural disasters and social conflict: A systematic literature review. International Journal of Disaster Risk Reduction 17:38-48. https://doi.org/10.1016/j.ijdrr.2016.04.001
Yalcin, A. and Bulut, F. 2007. Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Natural Hazards 41(1):201-226. https://doi.org/10.1007/s11069-006-9030-0
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. and 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
Yan, F., Zhang, Q., Ye, S. and Ren, B. 2019. A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology 327:170-187. https://doi.org/10.1016/j.geomorph.2018.10.024
Zillman, J. 1999. The Physical Impact of Disaster. Natural Disaster Management. Leicester: Tudor Rose Holdings Ltd. Leicester, 320.
Downloads
Submitted
Accepted
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Degraded and Mining Lands Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher. 
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Scientific Journal by Eko Handayanto is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://ub.ac.id.
Permissions beyond the scope of this license may be available at https://ircmedmind.ub.ac.id/.
This work is licensed under a 