Published

2025-10-29

Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan

Evaluación de la susceptibilidad de deslizamientos de tierra profundos en la región Galiat del sub-Himalaya, Pakistán

DOI:

https://doi.org/10.15446/esrj.v29n3.116114

Keywords:

Landslide inventory, deep-seated landslide, landslide susceptibility map, maximum entropy model (en)
inventario de deslizamientos de tierra, deslizamientos profundos, mapa de deslizamientos de tierra, modelo de máxima entropía (es)

Downloads

Authors

Landslides are the most prevalent natural hazards in the Sub-Himalayan region, posing extensive socio-economic losses. Their occurrence is highly influenced by weak geological formations, steep and dissected topography, irregular land-use, high seismic activity, and seasonal precipitation and snowmelt. Despite the high threat, there is an absence of landslide susceptibility maps for most of northern Pakistan, hindering effective measures for landslide hazard prevention. In this study, a relevant deep-seated landslide inventory for landslide susceptibility assessment of the Galiat Region was prepared based on field studies and multi-temporal Google Earth images, identifying 68 landslide polygons. Due to the localized nature of landslides, substantial predictions cannot be made with classical statistical modelling. Therefore, the landslide susceptibility map of the study area was modelled using the maximum entropy method, which allows predictions based on limited observational data. The analyses were repeated, with three randomly selected data sets being 30% and 70% for training and testing data, respectively. Fourteen environmental variables were considered, including geology, digital elevation model (DEM), and first and second DEM derivatives. The accuracy of the obtained models reached 0.80 ±0.002, evaluated by the AUC technique. The high to very high susceptible classes correspond to 26.16 % of the study area, including 74.3 % of the mapped landslides. The resultant landslide susceptibility map will raise understanding of dynamic and potential landslides for citizens, engineers, and land-use agencies.

Los deslizamientos de tierra son la amenaza natural más prevalente en la región del sub-Himalaya, y generan grandes perdidas socioeconómicas. Su ocurrencia está fuertemente influenciada por las débiles formaciones geológicas, la topografía inclinada y diseccionada, el uso irregular del suelo, la alta actividad sísmica y las lluvias estacionarias y el descongelamiento de nieve. A pesar de esta amenaza hacen falta mapas de susceptibilidad de deslizamientos de tierra para gran parte del norte de Pakistán, lo que dificulta la implementación efectiva de medidas para la mitigacion de estas amenazas. Para este estudio se preparó un inventario de evaluación de la susceptibilidad de deslizamientos de tierra profundos en la región de Galiat con base en estudios de campo y de imágenes multitemporales de Google Earth, donde se identificaron 68 polígonos de deslizamiento. Debido a la naturaleza localizada de los deslizamientos no se pudieron hacer predicciones sustanciales con los modelos estadísticos clásicos. Además, el mapa de susceptibilidad de deslizamientos de tierra del área de estudio se modeló con el método de máxima entropía, lo que permite predicciones con base en datos observacionales limitados. Los análisis se repitieron con tres secuencias de datos seleccionadas aleatoriamente, cada una con el 30 por ciento de entrenamiento y el 70 por ciento de prueba. Se consideraron 14 variables ambientales que incluyen geología, modelo de elevación digital, y la primera y segunda derivada de los modelos de elevación digital. La exactitud de los modelos obtenidos alcanzó 0.80 ± 0.002, según se evaluó con la técnica AUC. La clasificación de alta a muy alta susceptibilidad corresponde al 26.16 % del área de estudio, y que incluye el 74.3 % de los deslizamientos mapeados. El mapa de susceptibilidad de deslizamientos de tierra resultante incrementará el conocimiento de las dinámicas y potenciales deslizamientos para ciudadanos, ingenieros y agencias de uso del suelo.

References

Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11, 854-866. https://doi.org/10.3390/e11040854

Cao, C., Xu, P., Chen, J., Zheng, L., & Niu, C. (2017). Hazard assessment of debris-flow along the Baicha River in Heshigten Banner, Inner Mongolia, China. International Journal of Environmental Research and Public Health, 14, 30-30. https://doi.org/10.3390/ijerph14010030

Chen, W., Pourghasemi, H. R., Kornejady, A., & Zhang, N. (2017). Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma, 305, 314-327. https://doi.org/10.1016/j.geoderma.2017.06.020

Chen, X., Chen, H., You, Y., Chen, X., & Liu, J. (2016). Weights-of-evidence method based on GIS for assessing susceptibility to debris flows in Kangding County, Sichuan Province, China. Environmental Earth Sciences, 75, 1-16. https://doi.org/10.1007/s12665-015-5033-z

Chen, Z., & Wang, J. (2007). Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Natural Hazards, 42, 75-89. https://doi.org/10.1007/s11069-006-9061-6

Cömert, R., Avdan, U., & Gorum, T. (2018). Rapid mapping of forested landslide from ultra-high resolution unmanned aerial vehicle data. International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-3-W4-171-2018

Conoscenti, C., Ciaccio, M., Caraballo-Arias, N. A., Gómez-Gutiérrez, Á., Rotigliano, E., & Agnesi, V. (2015). Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy). Geomorphology, 242, 49-64. https://doi.org/10.1016/j.geomorph.2014.09.020

Convertino, M., Troccoli, A., & Catani, F. (2013). Detecting fingerprints of landslide drivers: A MaxEnt model. Journal of Geophysical Research: Earth Surface, 118, 1367-1386. https://doi.org/10.1002/jgrf.20099

Coward, M. P., Rex, D. C., Asif Khan, M., Windley, B. F., Broughton, R. D., Luff, I. W., Petterson, M. G., Pudsey, C. J. (1986). Collision tectonics in the NW Himalayas. Geological Society, London, Special Publications, 19, 203-219. https://doi.org/10.1144/GSL.SP.1986.019.01.11

Duman, T. Y. & Çan, T. (2023). Characteristics of landslides and assessment of deep-seated landslide susceptibility in Northern Turkey. Mediterranean Geoscience Reviews, 1-27. https://doi.org/10.1007/s42990-023-00105-3

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x

Farooq, S., & Malik, M. H. (1996). Landslide hazard management and control in Pakistan: A review. Kathmandu: ICIMOD, 68 p.

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, 85-98. https://doi.org/10.1016/j.enggeo.2008.03.022

Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94, 268-289. https://doi.org/10.1016/j.geomorph.2006.09.023

Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112, 42-66. https://doi.org/10.1016/j.earscirev.2012.02.001

Halvorsen, R. (2012). A gradient analytic perspective on distribution modelling. Sommerfeltia, 35, 1-165. https://doi.org/10.2478/v10208-011-0015-3

Iqbal, M., & Bannert, D. (1998). Structural observations of the Margala hills, Pakistan and the nature of the Main Boundary Thrust. Pakistan Journal of Hydrocarbon Research, 10, 41-53.

Jaynes, E. T. (1982). On the rationale of maximum-entropy methods. Proceedings of the IEEE, 70, 939-952.

Kamp, U., Growley, B. J., & Khattak, G. A., & Owen, L. A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology, 101, 631-642. https://doi.org/10.1016/j.geomorph.2008.03.003

Khan, M. R., Hameed, F., Mughal, M. S., Basharat, M., & Mustafa, S. (2016). Tectonic study of the Sub-Himalayas based on geophysical data in Azad Jammu and Kashmir and northern Pakistan. Journal of Earth Science, 27(6), 981–988. https://doi.org/10.1007/s12583-016-0681-9

Khan, M. R., & Ali, M. (1994). Preliminary modeling of the western Himalaya, Kashmir. Journal of Geology, 11, 59-66.

Kim, H. G., Lee, D. K., Park, C., Kil, S., Son, Y., & Park, J. H. (2015). Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios. Environmental Earth Sciences, 73, 1385-1400. https://doi.org/10.1007/s12665-014-3775-7

Kleidon, A., Malhi, Y., & Cox, P. M. (2010). Maximum entropy production in environmental and ecological systems. The Royal Society, 365. https://doi.org/10.1098/rstb.2010.0018

Kornejady, A., Ownegh, M., & Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162. https://doi.org/10.1016/j.catena.2017.01.010

Lee, S. (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, 1477-1491. https://doi.org/10.1080/01431160412331331012

Li, Y., Chen, J., Tan, C., Li, Y., Gu, F., Zhang, Y. & Mehmood, Q. (2020). Application of the borderline-SMOTE method in susceptibility assessments of debris flows in Pinggu District, Beijing, China. Natural Hazards. https://doi.org/10.1007/s11069-020-04409-7

Mehmood, Q., Qing, W., Chen, J., & Rahman, G. (2024). Predicting debris flow runout with SFLOW model: Case study of Datong and Taicun gullies, Lijiang, China. Geotechnical and Geological Engineering, 43(1), 47. https://doi.org/10.1007/s10706-024-03020-y

Mehmood, Q., Khan, A., Chen, J., Rahman, G., Nasrullah, & Tosunlu, H. (2023). Susceptibility assessment of landslide using Analytical Hierarchy Process and Weighted Overlay Analysis, along N-75 highway, Pakistan. Acta Geologica Slovaca, 15, 23-34.

Mehmood, Q., Qing, W., Chen, J., Yan, J., Ammar, M., Rahman, G., & Nasrullah (2021). Susceptibility assessment of single gully debris flow based on AHP and extension method. Civil Engineering Journal (Iran), 7. https://doi.org/10.28991/cej-2021-03091702.

Melo, R., Vieira, G., Caselli, A., & Ramos, M. (2012). Susceptibility modelling of hummocky terrain distribution using the information value method (Deception Island, Antarctic Peninsula). Geomorphology, 155, 88-95. https://doi.org/10.1016/j.geomorph.2011.12.027

Mert, A., Özkan, K., Şentürk, Ö., & Negiz, M. G. (2016). Changing the potential distribution of Turkey Oak (Quercus cerris L.) under climate change in Turkey. Polish Journal of Environmental Studies, 25, 1633-1638. https://doi.org/10.15244/pjoes/62230

Metz, C. E. (1978). Basic principles of ROC analysis. In Seminars in Nuclear Medicine (pp. 283-298). Elsevier.

Mughal, M. S., Zhang, C., Du, D., Zhang, L., Mustafa, S., Hameed, F., Khan, M. R., Zaheer, M., & Blaise, D. (2018). Petrography and provenance of the Early Miocene Murree Formation, Himalayan Foreland Basin, Muzaffarabad, Pakistan. Journal of Asian Earth Sciences, 162, 25-40. https://doi.org/10.1016/j.jseaes.2018.04.018

NASA. (2023). NASA POWER. https://power.larc.nasa.gov/data-access-viewer/

Niederer, S., & Schaffner, U. R. (1989). Landslide problems and erosion control in Murree and Kohat tehsils of Rawalpindi Dist: Results of the fact-finding mission. Swiss Development Cooperation, Ministry of Foreign Affairs, Government of Switzerland.

Niederer, S., Wagner, A., Khan, S. R., & Rafiq, M. (1989). Murree erosion control: Results of the facts-finding mission. SDC, Ministry of Foreign Affairs, Government of Switzerland.

Orefice, S., & Innocenti, C. (2025). Regional assessment of coastal landslide susceptibility in Liguria, Northern Italy, using MaxEnt. Natural Hazards, 121(3), 2613–2639. https://doi.org/10.1007/s11069-024-06833-5

Pandey, V. K., Pourghasemi, H. R., & Sharma, M. C. (2020). Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto International, 35, 168-187. https://doi.org/10.1080/10106049.2018.1510038

Park, N. W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73, 937-949. https://doi.org/10.1007/s12665-014-3442-z

Pearson, R. G., Raxworthy, C. J., Nakamura, M., & Townsend Peterson, A. (2007). Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102-117. https://doi.org/10.1111/j.1365-2699.2006.01594.x

Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., & Ferrier, S. (2009). Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecological Applications, 19, 181-197. https://doi.org/10.1890/07-2153.1

Phillips, S. J., Dudík, M., & Schapire, R. E. (2004). A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning (pp. 83-83). https://doi.org/10.1145/1015330.1015412

PMD. (2023). Pakistan Meteorological Department. In Rainfall Reports. Pakistan. https://www.pmd.gov.pk/en/

Pontius, R. G. Jr., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85, 239-248. https://doi.org/10.1016/S0167-8809(01)00187-6

Pourghasemi, H. R., & Rossi, M. (2017). Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: A comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology, 130, 609-633. https://doi.org/10.1007/s00704-016-1919-2

Rehman, G., Zhang, G., Rahman, M. U., Rahman, N. U., Usman, T., & Imraz, M. (2020). The engineering assessments and potential aggregate analysis of Mesozoic carbonates of Kohat Hills Range, KP, Pakistan. Acta Geodaetica et Geophysica, 55, 477-493. https://doi.org/10.1007/s40328-020-00301-9

Renner, I. W., & Warton, D. I. (2013). Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69, 274-281. https://doi.org/10.1111/j.1541-0420.2012.01824.x

Rosi, A., Frodella, W., Nocentini, N., Caleca, F., Havenith, H. B., Strom, A., Saidov, M., Bimurzaev, G. A., & Tofani, V. (2023). Comprehensive landslide susceptibility map of Central Asia. Natural Hazards and Earth System Sciences, 23, 2229-2250. https://doi.org/10.5194/nhess-23-2229-2023

Shannon, C. E. (1948). A mathematical theory of communication. Bell Technical Journal, 27, 379-423.

Sun, X., Chen, J., Bao, Y., Han, X., Zhan, J., & 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, 438. https://doi.org/10.3390/ijgi7110438

Tahirkheli, R. A. K. (1979). The India-Eurasia suture zone in northern Pakistan: Synthesis and interpretation of recent data at plate scale. Geodynamics of Pakistan, 125-130.

Tsangaratos, P., & Ilia, I. (2016). Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides, 13, 305-320. https://doi.org/10.1007/s10346-015-0565-6

Van Westen, C. J., Castellanos, E. & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102, 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010

Vorpahl, P., Elsenbeer, H., Märker, M. & Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27-39. https://doi.org/10.1016/j.ecolmodel.2011.12.007

Wu, S., Chen, J., Xu, C., Zhou, W., Yao, L., Yue, W., & Cui, Z. (2020). Susceptibility assessments and validations of debris-flow events in meizoseismal areas: Case study in China’s Longxi River watershed. Natural Hazards Review, 21, 05019005. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000347

Xiao, T., Segoni, S., Chen, L., Yin, K., & Casagli, N. (2020). A step beyond landslide susceptibility maps: A simple method to investigate and explain the different outcomes obtained by different approaches. Landslides, 17, 627-640. https://doi.org/10.1007/s10346-019-01299-0

Xiao, T., Yin, K., Yao, T., & Liu, S. (2019). Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China. Acta Geochimica, 38, 654-669. https://doi.org/10.1007/s11631-019-00341-1

Zeitler, P. K. (1985). Cooling history of the NW Himalaya, Pakistan. Tectonics, 4, 127-151.

Zhang, Y., Ge, T., Tian, W. & Liou, Y. A. (2019). Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sensing, 11, 2801. https://doi.org/10.3390/rs11232801

How to Cite

APA

Mehmood, Q. & Can, T. (2025). Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan. Earth Sciences Research Journal, 29(3), 261–273. https://doi.org/10.15446/esrj.v29n3.116114

ACM

[1]
Mehmood, Q. and Can, T. 2025. Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan. Earth Sciences Research Journal. 29, 3 (Oct. 2025), 261–273. DOI:https://doi.org/10.15446/esrj.v29n3.116114.

ACS

(1)
Mehmood, Q.; Can, T. Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan. Earth sci. res. j. 2025, 29, 261-273.

ABNT

MEHMOOD, Q.; CAN, T. Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan. Earth Sciences Research Journal, [S. l.], v. 29, n. 3, p. 261–273, 2025. DOI: 10.15446/esrj.v29n3.116114. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/116114. Acesso em: 26 dec. 2025.

Chicago

Mehmood, Qaiser, and Tolga Can. 2025. “Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan”. Earth Sciences Research Journal 29 (3):261-73. https://doi.org/10.15446/esrj.v29n3.116114.

Harvard

Mehmood, Q. and Can, T. (2025) “Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan”, Earth Sciences Research Journal, 29(3), pp. 261–273. doi: 10.15446/esrj.v29n3.116114.

IEEE

[1]
Q. Mehmood and T. Can, “Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan”, Earth sci. res. j., vol. 29, no. 3, pp. 261–273, Oct. 2025.

MLA

Mehmood, Q., and T. Can. “Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan”. Earth Sciences Research Journal, vol. 29, no. 3, Oct. 2025, pp. 261-73, doi:10.15446/esrj.v29n3.116114.

Turabian

Mehmood, Qaiser, and Tolga Can. “Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan”. Earth Sciences Research Journal 29, no. 3 (October 29, 2025): 261–273. Accessed December 26, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/116114.

Vancouver

1.
Mehmood Q, Can T. Susceptibility Assessment of Deep-seated Landslides in Sub-Himalayan Galiat Region, Pakistan. Earth sci. res. j. [Internet]. 2025 Oct. 29 [cited 2025 Dec. 26];29(3):261-73. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/116114

Download Citation

CrossRef Cited-by

CrossRef citations0

Dimensions

PlumX

Article abstract page views

72

Downloads

Download data is not yet available.