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Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016)

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Abstract

Landslides are one of the most important environmental hazards occur naturally or human-induced with large-scale social, economic, and environmental impacts. Landslide susceptibility zoning, which has been widely performed in the last decades, allows identifying spatial prediction of areas of landslides, which could be used for land use planning and land management. The present study was conducted as a review with the aim of investigating the research background of landslide susceptibility in the world during the period of 2005–2016. The results showed that the publication of papers related to landslide susceptibility during the period of investigation has been on the rise, and China has produced a larger number of papers and authors (13% of total). In addition, this article reviews the most popularly used models and the most frequently used input factors. Among different models, the logistic regression has been used as the most common method for assessing landslide susceptibility in 28.4% of the articles, and the slope gradient is considered as the most important conditioning factor in landslide occurrence in 94.2% of the articles. Finally, it is concluded that the recent technological developments in the field of remote sensing, computing technologies and Geographic Information Systems (GIS), the increased data availability, and the awareness has arisen among media and recent policy developments are important elements for increasing the research interest in landslide susceptibility.

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References

  • Abuzied S, Ibrahim S, Kaiser M, Saleem T (2016) Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt. J Mt Sci 13(7):1286–1303

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at northern Turkey. Arab J Geosci 9(2):165

    Article  Google Scholar 

  • Alcántara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology 47(2):107–124

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44

    Article  Google Scholar 

  • Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landf 26(12):1251–1263

  • Barrile V, Cirianni F, Leonardi G, Palamara R (2016) A fuzzy-based methodology for landslide susceptibility mapping. Procedia-Soc Behav Sci 223(10):896–902. https://doi.org/10.1016/j.sbspro.2016.05.309

    Article  Google Scholar 

  • Boslaugh S (2012) Statistics in a nutshell: a desktop quick reference. “O’Reilly Media, Inc.” Pages: 594

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. natural hazards and earth system sciences, 5:853–862. https://doi.org/10.5194/nhess-5-853-2005

  • Budimir MEA, Atkinson PM, Lewis HG (2014) Earthquake-and-landslide events are associated with more fatalities than earthquakes alone. Nat Hazards 72(2):895–914. https://doi.org/10.1007/s11069-014-1044-4

  • Budimir A, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12(3):419–436

    Article  Google Scholar 

  • Cárdenas NY, Mera EE (2016) Landslide susceptibility analysis using remote sensing and GIS in the western Ecuadorian Andes. Nat Hazards 81(3):1829–1859

    Article  Google Scholar 

  • Chen W, Ding X, Zhao R, Shi S (2016) Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City, China. Environ Earth Sci 75(1):64

    Article  Google Scholar 

  • Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 11(4):639–653

    Article  Google Scholar 

  • Crozier MJ (1986) Landslides: causes, consequences and environment. J R Soc N Z 18(3):343–343

    Google Scholar 

  • Dahal RK (2014) Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya. Environ Earth Sci 71(12):5145–5164

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):311–324

    Article  Google Scholar 

  • Dilley M (2005) Natural disaster hotspots: a global risk analysis, vol 5. World Bank Publications, p 148. http://documents.worldbank.org/curated/en/621711468175150317/pdf/344230PAPER0Na101official0use0only1.pdf

  • Dou J, Oguchi T, Hayakawa YS, Uchiyama S, Saito H, Paudel U (2014) GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. In: Landslide Science for a Safer Geoenvironment. Methods of Landslide Studies. Springer International Publishing. https://doi.org/10.1007/978-3-319-05050-8_65

  • Ercanoglu M (2005) Landslide susceptibility assessment of SE BartÕn (West Black Sea region, Turkey) by artificial neural networks. Nat Hazards Earth Syst Sci 5:979–992

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Gokceoglu C, Sezer E (2009) A statistical assessment on international landslide literature (1945–2008). Landslides 6(4):345–351

    Article  Google Scholar 

  • Günther A, Reichenbach P, Malet J-P, van Den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10(5):529–546

    Article  Google Scholar 

  • Hess DM, Leshchinsky BA, Bunn M, Mason HB, Olsen MJ (2017) A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity. Landslides:1–21

  • Hjort J, Luoto M (2013) Statistical methods for geomorphic distribution modeling, In: Treatise on Geomorphology, pp 59–73. https://doi.org/10.1016/b978-0-12-374739-6.00028-2

  • Holec J, Bednarik M, Sabo M, Minár J, Yilmaz I, Marschalko M (2013) A small-scale landslide susceptibility assessment for the territory of Western Carpathians. Nat Hazards 69(1):1081–1107

    Article  Google Scholar 

  • Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118

    Article  Google Scholar 

  • Hung LQ, Van NTH, Van Son P, Khanh NH, Binh LT (2016) Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam). Landslides 13(5):1285–1301

    Article  Google Scholar 

  • Ilia I, Koumantakis I, Rozos D, Koukis G, Tsangaratos P (2015) A Geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: the case study of Kimi, Euboea, Greece. In: Lollino G et al. (eds) Engineering Geology for Society and Territory, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-09057-3_210

  • Iovine GG, Greco R, Gariano SL, Pellegrino AD, Terranova OG (2014) Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Nat Hazards 73(1):111–136

    Article  Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Hazards 76(1):471–496

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

    Article  Google Scholar 

  • Kjekstad O, Highland L (2009) Economic and social impacts of landslides. In: Sassa K, Canuti P (eds) Landslides – Disaster Risk Reduction. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69970-5_30

  • Kritikos T, Davies T (2015) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western southern alps of New Zealand. Landslides 12(6):1051–1075

    Article  Google Scholar 

  • Lee JH, Park HJ, Jang JY (2017) Probabilistic analysis of shallow landslide susceptibility using physically based model and fuzzy point estimate method. In: Mikoš M, Casagli N, Yin Y, Sassa K (eds) Advancing Culture of Living with Landslides. WLF 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-53485-5_25

  • Manzo G, Tofani V, Segoni S, Battistini A, Catani F (2013) GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study. Int J Geogr Inf Sci 27:1433–1452

  • Meten M, PrakashBhandary N, Yatabe R (2015) Effect of landslide factor combinations on the prediction accuracy of landslide susceptibility maps in the Blue Nile Gorge of Central Ethiopia. Geoenviron Disasters 2(1):9

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Panagos P, Van Liedekerke M, Jones A, Montanarella L (2012) European Soil Data Centre: response to European policy support and public data requirements. Land Use Policy 29(2):329–338

    Article  Google Scholar 

  • Panagos P, Borrelli P, Meusburger K, Yu B, Klik A, Lim KJ, Yang JE, Ni J, Miao C, Chattopadhyay N, Sadeghi SHR et al (2017) Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Sci Rep 7:4175

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (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

    Article  Google Scholar 

  • Paulín GL, Pouget S, Bursik M, Quesada FA, Contreras T (2016) Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico. Nat Hazards 80(1):127–139

    Article  Google Scholar 

  • Pellicani R, Frattini P, Spilotro G (2014) Landslide susceptibility assessment in Apulian Southern Apennine: heuristic vs. statistical methods. Environ Earth Sci 72(4):1097–1108

    Article  Google Scholar 

  • Petley DN (2008) The global occurrence of fatal landslides in 2007. Geophys Res Abstr 10:EGU2008-A-10487

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75(3):1–17

    Article  Google Scholar 

  • Pourghasemi HR, Rossi M (2016) 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:1–25

  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013a) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122(2):349–369

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SF (2013b) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013c) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365

    Article  Google Scholar 

  • Pradhan AMS, Kim YT (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena 140:125–139

    Article  Google Scholar 

  • Rabonza ML, Felix RP, Lagmay AMFA, Eco RNC, Ortiz IJG, Aquino DT (2016) Shallow landslide susceptibility mapping using high-resolution topography for areas devastated by super typhoon Haiyan. Landslides 13(1):201–210

    Article  Google Scholar 

  • Saponaro A, Pilz M, Wieland M, Bindi D, Moldobekov B, Parolai S (2015) Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan. Bull Eng Geol Environ 74(4):1117–1136

    Article  Google Scholar 

  • Shahabi H, Khezri S, Ahmad BB, 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

    Article  Google Scholar 

  • Shahabi H, Hashim M, Ahmad BB (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

    Article  Google Scholar 

  • Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and land use, vol 18. https://doi.org/10.1029/WM018

  • Solaimani K, Mousav SZ, Kavian A (2013) Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab J Geosci 6(7):2557–2569

  • Steger S, Brenning A, Bell R, Petschko H, Glade T (2016) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23

    Article  Google Scholar 

  • Süzen ML, Kaya BŞ (2012) Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digital Earth 5(4):338–355

    Article  Google Scholar 

  • Teerarungsigul S, Torizin J, Fuchs M, Kühn F, Chonglakmani C (2016) An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, northern Thailand. Landslides 13(5):1151–1165

    Article  Google Scholar 

  • Tien Bui D, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016a) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75(14):1101

    Article  Google Scholar 

  • Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016b) 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(2):361–378

    Article  Google Scholar 

  • Timilsina M, Bhandary NP, Dahal RK, Yatabe R (2014) Distribution probability of large-scale landslides in Central Nepal. Geomorphology 226:236–248

    Article  Google Scholar 

  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136

    Article  Google Scholar 

  • Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179

    Article  Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3):112–131

    Article  Google Scholar 

  • Wang LJ, Guo M, Sawada K, Lin J, Zhang J (2015) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 135:271–282

    Article  Google Scholar 

  • Wang Q, Li W, Wu Y, Pei Y, Xie P (2016) Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China). Environ Earth Sci 75(7):599

    Article  Google Scholar 

  • Wu X, Ren F, Niu R (2014) Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci 71(11):4725–4738

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, El-Haddad BA, Dhahry BK (2016a) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia. Bull Eng Geol Environ 75(1):63–87

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016b) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856

    Article  Google Scholar 

  • Zhang G, Cai Y, Zheng Z, Zhen J, Liu Y, Huang K (2016) Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena 142:233–244

    Article  Google Scholar 

  • Zhu AX, Wang R, Qiao J, Qin CZ, Chen Y, Liu J, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138

    Article  Google Scholar 

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Acknowledgments

The authors would like to appreciate from reviewers and Editor-in-Chief Comments.

Funding

The study was supported by College of Agriculture, Shiraz University (Grant no. 96GRD1M271143).

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. Department of Geomorphology, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran

    Zeinab Teimoori Yansari

  3. Joint Research Centre, European Commission, Ispra, Varese, Italy

    Panos Panagos

  4. School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, New South Wales, 2007, Australia

    Biswajeet Pradhan

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  1. Hamid Reza Pourghasemi
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Correspondence to Hamid Reza Pourghasemi.

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Pourghasemi, H.R., Teimoori Yansari, Z., Panagos, P. et al. Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11, 193 (2018). https://doi.org/10.1007/s12517-018-3531-5

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