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Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi-criteria decision-making method

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Abstract

This study maps the landslide susceptibility along the China–Pakistan Economic Corridor (CPEC) route using multiple landslide causative factors. A decisive statistical approach and the Geographic Information System (GIS) were used to map the route’s susceptibility. The study area comprises a 236 km section of the Karakoram Highway, located in a region subjected to repeated landslides. The maps of different causative factors, including topographical, geological, and hydrological factors, were generated through GIS using data obtained from various sources. The causative factors were weighted according to their potential for developing a landslide event in a pairwise matrix of a multi-criteria decision-making approach. The analytical hierarchy process was applied to get the Consistency Index that governed the whole rating process. The weights of the landslide causative factors were used for generating the study area’s final landslide susceptibility map. The results indicated that about 38% of the study area falls under the category of high and very high susceptibility. The outcomes of this study could be valuable in the identification of the parameters at a given area or region that are more prominently influencing the happening of landslides, in this way permitting the more viable preventive measures to be taken.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12(6):1077–1095

    Google Scholar 

  • Ahmed MF et al (2014) A regional level preliminary landslide susceptibility study of the upper Indus river basin. Eur J Remote Sens 47(1):343–373

    Google Scholar 

  • Akgun A et al (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143

    Google Scholar 

  • Ali S et al (2019) Landslide susceptibility mapping by using a geographic information system (GIS) along the China-Pakistan Economic Corridor (Karakoram Highway), Pakistan. Nat Hazards Earth Syst Sci 19(5):999–1022

    Google Scholar 

  • Alimohammadlou Y et al (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. CATENA 120:149–162

    Google Scholar 

  • Anderson JR (1971) Land-use classification schemes. Photogramm Eng 37(4):379–387

    Google Scholar 

  • Apurv T et al (2015) Impact of climate change on floods in the Brahmaputra basin using CMIP5 decadal predictions. J Hydrol 527:281–291

    Google Scholar 

  • Arnoldus H (1980) An approximation of the rainfall factor in the universal soil loss equation. An approximation of the rainfall factor in the universal soil loss equation. Wiley, Chichester, pp 127–132

    Google Scholar 

  • Ayalew L et al (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4):432–445

    Google Scholar 

  • Basharat M et al (2016) Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian J Geosci 9(4):1–19

    Google Scholar 

  • Calligaris C et al (2013) First steps towards a landslide inventory map of the Central Karakoram National Park. Eur J Remote Sens 46(1):272–287

    Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252

    Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962

    Google Scholar 

  • Chen W et al (2016) Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci 75(20):1–15

    Google Scholar 

  • Cui Y et al (2019) The cost of rapid and haphazard urbanization: lessons learned from the Freetown landslide disaster. Landslides 16(6):1167–1176

    Google Scholar 

  • Dehnavi A et al (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122–148

    Google Scholar 

  • Demir G (2019) GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). CATENA 183:104211

    Google Scholar 

  • Ding M et al (2018) Surge-type glaciers in Karakoram Mountain and possible catastrophes alongside a portion of the Karakoram Highway. Nat Hazard 90(2):1017–1020

    Google Scholar 

  • DiPietro JA, Pogue KR (2004) Tectonostratigraphic subdivisions of the Himalaya: a view from the west. Tectonics. https://doi.org/10.1029/2003TC001554

    Article  Google Scholar 

  • Ekumah B et al (2020) Geospatial assessment of ecosystem health of coastal urban wetlands in Ghana. Ocean Coast Manag 193:105226

    Google Scholar 

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75(3–4):229–250

    Google Scholar 

  • Feizizadeh B, Blaschke T (2011) Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. J Earth Sci Eng 1(1):66–77

    Google Scholar 

  • Fell R et al (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102(3–4):99–111

    Google Scholar 

  • Fressard M et al (2014) Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d’Auge plateau hillslopes (Normandy, France). Nat Hazards Earth Syst Sci 14(3):569–588

    Google Scholar 

  • Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18(8):2161–2181

    Google Scholar 

  • Goudie A et al (1984) The geomorphology of the Hunza valley, Karakoram mountains, Pakistan. In: The international Karakoram project. International conference

  • Greco R et al (2007) Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy. Eng Geol 89(1–2):47–66

    Google Scholar 

  • Guo Z et al (2017) Hazard assessment of potentially dangerous bodies within a cliff based on the Fuzzy-AHP method: a case study of the Mogao Grottoes, China. Bull Eng Geol Environ 76(3):1009–1020

    Google Scholar 

  • Guzzetti F et al (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

    Google Scholar 

  • Hewitt K (1998) Catastrophic landslides and their effects on the Upper Indus streams, Karakoram Himalaya, northern Pakistan. Geomorphology 26(1–3):47–80

    Google Scholar 

  • Hong H et al (2017) A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 290:1–16

    Google Scholar 

  • Hong H et al (2018) Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides 15(4):753–772

    Google Scholar 

  • Horn BK (1981) Hill shading and the reflectance map. Proc IEEE 69(1):14–47

    Google Scholar 

  • Jade S et al (2004) GPS measurements from the Ladakh Himalaya, India: preliminary tests of plate-like or continuous deformation in Tibet. Geol Soc Am Bull 116(11–12):1385–1391

    Google Scholar 

  • Kamp U et al (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101(4):631–642

    Google Scholar 

  • Kanwal S et al (2017) GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins”. Geomat Nat Hazards Risk 8(2):348–366

    Google Scholar 

  • Khan H et al (2019) Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egypt J Remote Sens Space Sci 22(1):11–24

    Google Scholar 

  • Kirschbaum D et al (2015) Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4–15

    Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113

    Google Scholar 

  • Mandal B, Mandal S (2018) Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Adv Space Res 62(11):3114–3132

    Google Scholar 

  • Mondal S, 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. Int J Disaster Risk Sci 4(4):200–212

    Google Scholar 

  • Moosavi V, Niazi Y (2016) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114

    Google Scholar 

  • Nascimento KRDS, Alencar MH (2016) Management of risks in natural disasters: a systematic review of the literature on NATECH events. J Loss Prev Process Ind 44:347–359

    Google Scholar 

  • Oh H-J, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Google Scholar 

  • Park S et al (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68(5):1443–1464

    Google Scholar 

  • Paulín GL et al (2013) An overview of a GIS method for mapping and assessing landslide hazards. Landslide science and practice. Springer, Berlin, pp 379–385

    Google Scholar 

  • Pavelsky TM, Smith LC (2008) RivWidth: a software tool for the calculation of river widths from remotely sensed imagery. IEEE Geosci Remote Sens Lett 5(1):70–73

    Google Scholar 

  • Pourghasemi HR, 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. Theor Appl Climatol 130(1):609–633

    Google Scholar 

  • Pourghasemi HR et al (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazard 63(2):965–996

    Google Scholar 

  • Pourghasemi HR et al (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Nat Hazards Risk 7(2):861–885

    Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4(1):1–15

    Google Scholar 

  • Pradhan B et al (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1(3):199–223

    Google Scholar 

  • Pradhan B et al (2017) Performance evaluation and sensitivity analysis of expert-based, statistical, machine learning, and hybrid models for producing landslide susceptibility maps. Laser scanning applications landslide assessment. Springer, Cham, pp 193–232

    Google Scholar 

  • Ray R, De Smedt F (2009) Slope stability analysis on a regional scale using GIS: a case study from Dhading, Nepal. Environ Geol 57(7):1603–1611

    Google Scholar 

  • Refice A, Capolongo D (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci 28(6):735–749

    Google Scholar 

  • Reichenbach P et al (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manag 54(6):1372–1384

    Google Scholar 

  • Restrepo C, Alvarez N (2006) Landslides and their contribution to land-cover change in the mountains of Mexico and Central America 1. Biotropica 38(4):446–457

    Google Scholar 

  • Saaty RW (1987) The analytic hierarchy process—what it is and how it is used. Math Model 9(3–5):161–176

    Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26

    Google Scholar 

  • Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  • Saha S et al (2019) Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India. Environ Earth Sci 78(23):1–18

    Google Scholar 

  • Samia J et al (2017) Characterization and quantification of path dependency in landslide susceptibility. Geomorphology 292:16–24

    Google Scholar 

  • Schilirò L et al (2016) Prediction of shallow landslide occurrence: validation of a physically-based approach through a real case study. Sci Total Environ 569:134–144

    Google Scholar 

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5(1):1–15

    Google Scholar 

  • Shahabi H et al (2012) Application of satellite remote sensing for detailed landslide inventories using frequency ratio model and GIS. Int J Comput Sci 9:108–117

    Google Scholar 

  • Shahabi H et al (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environ Earth Sci 73(12):8647–8668

    Google Scholar 

  • Shahri AA et al (2019) Landslide susceptibility hazard map in southwest Sweden using artificial neural network. CATENA 183:104225

    Google Scholar 

  • Sidle R, Ochiai H (2006) Processes, prediction, and land use. Water resources monograph. American Geophysical Union, Washington

    Google Scholar 

  • Suh J et al (2011) National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environ Earth Sci 63(3):619–632

    Google Scholar 

  • Sujatha ER et al (2014) Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Eng Geol Environ 73(1):147–161

    Google Scholar 

  • Umar Z et al (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

    Google Scholar 

  • Van Westen C et al (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65(2):167–184

    Google Scholar 

  • Wang Q et al (2015) GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J Earth Syst Sci 124(7):1399–1415

    Google Scholar 

  • Wang Q et al (2016) Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models. Geosci J 20(5):705–718

    Google Scholar 

  • Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87(4):352–364

    Google Scholar 

  • Wentworth CK (1930) A simplified method of determining the average slope of land surfaces. Am J Sci 5(117):184–194

    Google Scholar 

  • Wu Y et al (2016) Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 75(5):422

    Google Scholar 

  • Xu J et al (2016) Natural disasters and social conflict: a systematic literature review. Int J Disaster Risk Reduct 17:38–48

    Google Scholar 

  • 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

    Google Scholar 

  • Yan F et al (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

    Google Scholar 

  • Zeitler PK (1985) Cooling history of the NW Himalaya, Pakistan. Tectonics 4(1):127–151

    Google Scholar 

  • Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Process Landf 12(1):47–56

    Google Scholar 

  • Zhiquan Y et al (2016) Types and space distribution characteristics of debris flow disasters along China–Pakistan Highway. Electron J Geotech Eng 21:191–200

    Google Scholar 

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Correspondence to Umer Khalil.

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Maqsoom, A., Aslam, B., Khalil, U. et al. Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi-criteria decision-making method. Model. Earth Syst. Environ. 8, 1519–1533 (2022). https://doi.org/10.1007/s40808-021-01226-0

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