Abstract
The geological and geographical conditions have made the Himalayas one of the regions most vulnerable to disasters. Northern Pakistan is made up of high-relief Himalayan mountains where landslides are the most frequently occurring catastrophic events. Floods, earthquakes, and landslides occur most often in this region, causing deaths and severe infrastructure damages. The studied area is experiencing significant landslide events triggered by destructive seismic activity, extreme seasonal rainfalls, and human activities. In this study, landslide susceptibility mapping is proposed using an integrated model of information value method by optimizing its capabilities and overcoming its limitations to enhance both precision and accuracy of the results. This approach quantifies and estimates the probability of landslide occurrence using statistical principles and techniques. Seven distinct landslide causative factors, including slope angle, distance to stream, drainage density, distance to road, land cover, normalized difference vegetation index, lithology, and distance to faults were evaluated to determine the areas at risk for future slope movements. A reliable landside inventory from a spatial database combined with ground-truthing field surveys was prepared for evaluating the relationship between the landslides and their causative factors. The accuracy and efficiency of the developed model are evaluated using sensitivity analysis of the receiver operator curve (ROC). The ROC-validated results showed an accuracy of 0.845 for model prediction rate and an accuracy of 0.837 for model success rate, indicating that the model is reliable to produce landslide susceptibility map. The results showed the reasonable efficiency of the applied method for assessing landslide susceptibility in the study area and applicable to other regions with similar geomorphological conditions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10:194. https://doi.org/10.1007/s12517-017-2980-6
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Afungang RN, de Meneses Bateira CV, Nkwemoh CA (2017) Assessing the spatial probability of landslides using GIS and informative value model in the Bamenda highlands. Arab J Geosci 10:384. https://doi.org/10.1007/s12517-017-3155-1
Akram MS, Ahmed L, Farooq S et al (2018) Geotechnical evaluation of rock cut slopes using basic rock mass rating (RMR basic), slope mass rating (SMR) and kinematic analysis along Islamabad Muzaffarabad Dual Carriageway (IMDC), Pakistan. J Biodivers Environ Sci 13:297–306
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44. https://doi.org/10.1007/s100640050066
Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277. https://doi.org/10.1016/0013-7952(92)90053-2
Rahman AU, Khan A, Shaw R (2015) Disaster risk reduction approaches in Pakistan. Springer Verlag
Bahrami S, Rahimzadeh B, Khaleghi S (2019) Analyzing the effects of tectonic and lithology on the occurrence of landslide along Zagros ophiolitic suture: a case study of Sarv-Abad, Kurdistan. Iran Bull Eng Geol Environ 79:1–19. https://doi.org/10.1007/s10064-019-01639-3
Banshtu RS, Versain LD, Pandey DD (2020) Risk assessment using quantitative approach: central Himalaya, Kullu, Himachal Pradesh, India. Arab J Geosci 13:1–11. https://doi.org/10.1007/s12517-020-5143-0
Bossart P, Ottiger R (1989) Rocks of the Murree formation in northern Pakistan: indicators of a descending foreland basin of late Paleocene to middle Eocene age. Eclogae Geol Helv 82:133–165
Bui DT, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444. https://doi.org/10.1007/s11069-011-9844-2
Bui DT, Tuan TA, Klempe H et al (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 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423. https://doi.org/10.1007/s10346-010-0202-3
Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75:867. https://doi.org/10.1007/s12665-016-5317-y
Chen W, Fan L, Li C, Pham BT (2020) Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in nanzheng county, China. Appl Sci 10:29. https://doi.org/10.3390/app10010029
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena. 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:1389–1399
Corominas J, Van Westen CJ, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (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. Nat Hazards 65:135–165. https://doi.org/10.1007/s11069-012-0347-6
Dikshit A, Sarkar R, Pradhan B, Acharya S, Alamri AM (2020) Spatial landslide risk assessment at Phuentsholing, Bhutan. Geosciences 10:131. https://doi.org/10.3390/geosciences10040131
Du G, Zhang Y, Iqbal J et al (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14:249–268. https://doi.org/10.1007/s11629-016-4126-9
Dunning SA, Mitchell WA, Rosser NJ, Petley DN (2007) The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir earthquake of 8 October 2005. Eng Geol 93:130–144. https://doi.org/10.1016/j.enggeo.2007.07.003
Felicísimo ÁM (2003) Bonham-Carter, GF (1996): geographic information systems for geoscientists. Modelling with GIS. GeoFocus Rev Int Cienc Tecnol la Inf Geográfica:9–12
Francioni M, Calamita F, Coggan J et al (2019) A multi-disciplinary approach to the study of large rock avalanches combining remote sensing, GIS and field surveys: the case of the Scanno landslide, Italy. Remote Sens 11:1570. https://doi.org/10.3390/rs11131570
Freeman P, Martin L, Mechler R, Warner K (2004) A methodology for incorporating natural catastrophes into macroeconomic projections. Disaster Prev Manag An Int J 13:337–342. https://doi.org/10.1108/09653560410556564
Gill JC, Malamud BD (2017) Anthropogenic processes, natural hazards, and interactions in a multi-hazard framework. Earth-Sci Rev 166:246–269. https://doi.org/10.1016/j.earscirev.2017.01.002
Guo C, Montgomery DR, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110. https://doi.org/10.1016/j.geomorph.2015.07.012
Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas. Eng Geol 28:119–131. https://doi.org/10.1016/0013-7952(90)90037-2
Guri PK, Patel RC (2015) Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling. Environ Monit Assess 187:324. https://doi.org/10.1007/s10661-015-4535-1
Guzzetti F (2002) Landslide hazard assessment and risk evaluation: limits and prospectives. In: Proceedings 4th Plinius conference on Mediterranean storms. Universitat de Illes Baleares, Mallorca, Spain
Harp EL, Crone AJ (2006) Landslides triggered by the October 8, 2005, Pakistan earthquake and associated landslide-dammed reservoirs. U. S. Geological Survey Open-File Report 1–13
Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, china. Arab J Geosci 9:112. https://doi.org/10.1007/s12517-015-2094-y
Huang F, Chen J, Du Z et al (2020) Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. ISPRS Int J Geo-Information 9:377. https://doi.org/10.3390/ijgi9060377
Jade S, Sarkar S (1993) Statistical models for slope instability classification. Eng Geol 36:91–98. https://doi.org/10.1016/0013-7952(93)90021-4
Kamp U, Growley BJ, Khattak GA, Owen LA (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
Kaur H, Gupta S, Parkash S, Thapa R, Gupta A, Khanal GC (2019) Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques. Ann GIS 25:113–132. https://doi.org/10.1080/19475683.2019.1575906
Kelarestaghi A, Ahmadi H (2009) Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95–101. https://doi.org/10.1007/s12517-008-0022-0
Khan AN, Collins AE, Qazi F (2011) Causes and extent of environmental impacts of landslide hazard in the Himalayan region: a case study of Murree, Pakistan. Nat Hazards 57:413–434. https://doi.org/10.1007/s11069-010-9621-7
Khattak GA, Owen LA, Kamp U, Harp EL (2010) Evolution of earthquake-triggered landslides in the Kashmir Himalaya, northern Pakistan. Geomorphology 115:102–108. https://doi.org/10.1016/j.geomorph.2009.09.035
Lee D-H, Kim Y-T, Lee S-R (2020) Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sens 12:1194. https://doi.org/10.3390/rs12071194
Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, NE Algeria. Arab J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5
Mahmood I, Qureshi SN, Tariq S, Atique L, Iqbal MF (2015) Analysis of landslides triggered by October 2005, Kashmir Earthquake. PLoS Curr:7. https://doi.org/10.1371/currents.dis.0bc3ebc5b8adf5c7fe9fd3d702d44a99
Martha TR, Van Westen CJ, Kerle N et al (2013) Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology 184:139–150. https://doi.org/10.1016/j.geomorph.2012.12.001
Meena SR, Ghorbanzadeh O, Blaschke T (2019) A comparative study of statistics-based landslide susceptibility models: a case study of the region affected by the Gorkha earthquake in Nepal. ISPRS Int J Geo-Information 8:94. https://doi.org/10.3390/ijgi8020094
Meena SR, Gudiyangada Nachappa T (2019) Impact of spatial resolution of digital elevation model on landslide susceptibility mapping: a case study in Kullu Valley, Himalayas. Geosciences 9:360. https://doi.org/10.3390/geosciences9080360
Nath SK (2004) Seismic hazard mapping and microzonation in the Sikkim Himalaya through GIS integration of site effects and strong ground motion attributes. Nat Hazards 31:319–342. https://doi.org/10.1023/B:NHAZ.0000023355.18619.0c
Pascaline W, Rowena H (2018) Economic losses, poverty & disasters: 1998-2017 [M]. United Nations Off Disaster Risk Reduct. https://doi.org/10.1002/9781119359203.ch3
Petley D, Dunning S, Rosser N, Kausar AB (2006) Incipient landslides in the Jhelum Valley, Pakistan following the 8th October 2005 earthquake. Disaster Mitigation of Debris Flows, Slope Failures and Landslides. Frontiers of Science Series, Universal Academy Press, Tokyo, 47:47–56
Pradhan AMS, Dawadi A, Kim YT (2012) Use of different bivariate statistical landslide susceptibility methods: a case study of Khulekhani watershed, Nepal. J Nepal Geol Soc 44:1–12
Rai PK, Mohan K, Kumra VK (2014) Landslide hazard and its mapping using remote sensing and GIS. J Sci Res 58:1–13
Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River basin using logistic regression model. Nat Hazards 85:1323–1346. https://doi.org/10.1007/s11069-016-2591-7
Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters 3:19. https://doi.org/10.1186/s40677-016-0053-x
Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187. https://doi.org/10.1016/j.geomorph.2009.10.002
Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69. https://doi.org/10.1007/s10346-004-0039-8
Saha S, Saha A, Hembram TK, Pradhan B, Alamri AM (2020) Evaluating the performance of individual and novel ensemble of machine learning and statistical models for landslide susceptibility assessment at Rudraprayag District of Garhwal Himalaya. Appl Sci 10:3772. https://doi.org/10.3390/app10113772
Sarkar S, Kanungo DP, Patra AK, Kumar P (2012) GIS Based Landslide Susceptibility Mapping–A Case Study in Indian Himalaya. In: Disaster Mitigation of Debris Flows, Slope Failures and Landslides. Universal Academy Press, Inc. Tokyo, Japan, p 838
Sarkar S, Roy AK, Martha TR (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India 82:351–362. https://doi.org/10.1007/s12594-013-0162-z
Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6:9600–9652. https://doi.org/10.3390/rs6109600
Schneider JF (2009) Seismically reactivated Hattian slide in Kashmir, Northern Pakistan. J Seismol 13:387–398. https://doi.org/10.1007/s10950-008-9103-5
Shafique M, van der Meijde M, Khan MA (2016) A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective. J Asian Earth Sci 118:68–80. https://doi.org/10.1016/j.jseaes.2016.01.002
Shahabi H, Ahmad BB, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci 6:3885–3907. https://doi.org/10.1007/s12517-012-0650-2
Shu H, Hürlimann M, Molowny-Horas R, González M, Pinyol J, Abancó C, Ma J (2019) Relation between land cover and landslide susceptibility in Val d’Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction. Sci Total Environ 693:133557. https://doi.org/10.1016/j.scitotenv.2019.07.363
Skidmore A (2002) Environmental modelling with GIS and remote sensing: introduction, 1st edn. CRC Press
Tibaldi A, Ferrari L, Pasquarè G (1995) Landslides triggered by earthquakes and their relations with faults and mountain slope geometry: an example from Ecuador. Geomorphology 11:215–226. https://doi.org/10.1016/0169-555X(94)00060-5
Torizin J, Fuchs M, Awan AA, Ahmad I, Akhtar SS, Sadiq S, Razzak A, Weggenmann D, Fawad F, Khalid N, Sabir F, Khan AJ (2017) Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan. Nat Hazards 89:757–784. https://doi.org/10.1007/s11069-017-2992-2
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e
Van Westen CJ, Van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65:167–184. https://doi.org/10.1007/s10064-005-0023-0
Wang J, Yin K, Xiao L (2014) Landslide susceptibility assessment based on GIS and weighted information valuea case study of Wanzhou district, three gorges reservoir. Chin J Rock Mech Eng 33:797–808
Weng Q (2010) Remote sensing and GIS integration: theories, methods, and applications. McGraw-Hill, New York
Wittich KP, Hansing O (1995) Area-averaged vegetative cover fraction estimated from satellite data. Int J Biometeorol 38:209–215. https://doi.org/10.1007/BF01245391
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. Arab J Geosci 9:84. https://doi.org/10.1007/s12517-015-2112-0
Yalcin A, Reis S, Aydinoglu AC, 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 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014
Yang Z, Lan H, Gao X, Li LP, Meng YS, Wu YM (2015) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75:2467–2487. https://doi.org/10.1007/s11069-014-1441-8
Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836. https://doi.org/10.1007/s12665-009-0394-9
Yin KL, Yan TZ (1988) Statistical prediction models for instability of metamorphosed rocks. In: Proceedings of 5th Int Symp on Landslides, Lausanne. pp 1269–1272
Yu L, Cao Y, Zhou C, Wang Y, Huo Z (2019) Landslide susceptibility mapping combining information gain ratio and support vector machines: a case study from Wushan segment in the three gorges reservoir area, China. Appl Sci 9:4756. https://doi.org/10.3390/app9224756
Zhao C, Chen W, Wang Q, Wu Y, Yang B (2015) A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China. Arab J Geosci 8:9079–9088. https://doi.org/10.1007/s12517-015-1891-7
Zhao X, Chen W (2020a) GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl Sci 10:16. https://doi.org/10.3390/app10010016
Zhao X, Chen W (2020b) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12:2180
Zvelebil J, Šíma J, Vilímek V (2010) Geo-risk management for developing countries—vulnerability to mass wasting in the Jemma River basin, Ethiopia. Landslides 7:99–103. https://doi.org/10.1007/s10346-009-0191-2
Acknowledgements
Special thanks to the Geological Survey of Pakistan, Islamabad office, for providing data related to the study area. The authors would like to thank the editors and the reviewers for their valuable contributions.
Author information
Authors and Affiliations
Contributions
Salman Farooq and Mian Sohail Akram conceptualized and planned the research. Salman Farooq contributed significantly to perform the experiment, data analysis, and validation of results. Salman Farooq took the lead in writing the manuscript, and Mian Sohail Akram supervised the work. The final manuscript was read and accepted by both authors.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Farooq, S., Akram, M.S. Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arab J Geosci 14, 824 (2021). https://doi.org/10.1007/s12517-021-07147-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07147-7