Abstract
Landslides are one of the most catastrophic geo-risks observed in northern Algeria, particularly in the regions of mountain ranges (Mediterranean Kabylia), where the processes are spectacular. During the last decade, the risk of landslides has increased more and more in urban space, mainly affecting the economy and human life. The extension of urbanization aggravates natural risks and creates additional artificial risks. The present work aims to identify the zones of susceptibility to landslides for the Mediterranean Kabylia, in North Algeria, by using multivariate statistical analysis. Landslide inventory map of the study area was prepared based on 84 landslides. The seven parameters, (1) elevation, (2) lithology, (3) slope gradient, (4) NDVI (normalized difference vegetation index), (5) rainfall, (6) land use, and (7) human cause category, were prepared and were considered to be independent factors. The weights are assigned to each factor after removing the multicollinearity in the dataset using principal component analysis (PCA). The model’s landslide susceptibility maps were validated using (i) relative landslide density index, (ii) success rate, and (iii) predictive rate curves. The results of the analysis show that the moderate, high, and very high susceptibility classes to landslide density indexes represented 45.45%, 71.43%, and 83.33%, respectively. The AUC (area under the curve) value is 0.782 indicating good model precision for identifying susceptible areas. The selection of parameters conditioning landslides is carefully made and even justified for a large number of these parameters. The PCA analysis also shows a good effect to remove multicollinearity of the parameters. Moreover, this model constitutes a first approach for assessing and planning landslides in Algeria and can be recommended for mapping the susceptibility to landslides in other regions.
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References
Atkinson PM, Massari R (1998) Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy". Comput Geosci 24:373–385
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31
Barlow J, Martin Y, Franklin SE (2003) Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British Columbia. Can J Remote Sens 29:510–517
Bonnard C, Forlati F, Scavia C (2004) Identification and mitigation of large landslide risks in Europe. Advances in risk assessment, IMIRILAND Project, Leiden, Balkema
Brabb E, Malgot J, Mahr T (1979) Engineering geological mapping of the West Carpathian landslide areas. Bull In Assoc Eng Geol-Bulletin De L’association Internationale De Géologie De L’ingénieur 19(1):116–121
Brabb EE, Pampeyan EH (1972) Preliminary map of landslide deposits in San Mateo County. California 344
Canuti P, Casagli N, Fanti R (2003) Landslide hazard for archaeological heritage: the case of Tharros in Italy. Landslides News 14(15):40–43
Carrara A, Bitelli G, Carla R (1997) Comparison of techniques for generating digital terrain models from contour lines. Int J Geogr Inf Sci 11(5):451–473
Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard Geographical information systems in assessing natural hazards, Springer Netherlands, 1995:135-175
Carrara A (1983) Multivariate models for landslide hazard evaluation, In: Mathematical Geology 15, pp 403–426
Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833
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
Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:1389–1399
Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472
Corominas J, Van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263
Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38(5):911–923
Dehnavi A, Aghdam IN, Pradhan B, MorshedVarzandeh MH (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neurofuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122–148
Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M et al (2020) Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17:1897–1914. https://doi.org/10.1007/s10346-020-01392-9
Djerbal L, Alimrina N, Melbouci B, Bahar R (2014) Mapping and management of landslide risk in the city of Azazga (Algeria). In: Sassa K, Canuti P, Yin Y (eds) Landslide Science for a Safer Geoenvironment. Springer, Cham. https://doi.org/10.1007/978-3-319-05050-8_72
Djerbal L, Khoudi I, Alimrina N, Melbouci B, Bahar R (2017) Assessment and mapping of earthquake-induced landslides in Tigzirt City, Algeria. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards 87(3), 1859-1879
Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346
Fedra K (1997) Decision support for natural resources management: models, GIS and expert systems. IIASA Laxenburg, Austria
Gemache MA, Machane D, Beldjoudi H, Gabri S, Djadia L, Benahmed S, Ymmel H (2010) On a damaging earthquake-induced landslide in the Algerian Alps: the March 20, 2006 Laâlam landslide (Babors chain, northeast Algeria), triggered by the Kherrata earthquake (Mw = 5,3). Nat Hazard 54:273–288
Getachew N, Meten M (2021) Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenviron Disasters 8-6
Gómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27
Gorsevski P (2001) Statistical modeling of landslide hazard using GIS, Proceedings of the Seventh Federal Interagency Sedimentation. Conference, March 25 to 29, 2001, Reno, Nevada: 103–109
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
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
Hu Q, Zhou Y, Wang S, Wang F (2020) Machine learning and fractal theory models for landslide susceptibility mapping: case study from the Jinsha River Basin. Geomorphology 351:106975
IBM Corp. Released (2017) IBM SPSS statistics for windows, Version 25.0. Armonk, NY: IBM Corp
Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hubl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ 653:801–814
Kab A, Djerbal L, Bahar R (2017) Landslide susceptibility mapping of Tizi-Ouzou Region, Algeria. In: Wasowski J, Giordan D, Lollino P (eds) Engineering Geology and Geological Engineering for Sustainable Use of the Earth’s Resources, Urbanization and Infrastructure Protection from Geohazards. GeoMEast 2017. Sustainable Civil Infrastructures. Springer, Cham. https://doi.org/10.1007/978-3-319-61648-3_10
Kienholz H (1978) Maps of geomorphology and natural hazards of Grindelwald, Switzerland: scale 1: 10,000. Arct Alpine Res 169–184
Lebart L, Morineau A, Piron M (2000) Statistique Exploratoire Multidimensionnelle. Dunod
Lee S, Evangelista DG (2006) Earthquake-induced landslidesusceptibility mapping using an artificial neural network. Nat. Hazards Earth Syst. Sci. 6: 687–695. http://www.nathazards-earth-syst-sci.net/6/687/2006/
Ling S, Zhao S, Huang J, Zhang X (2022) Landslide susceptibility assessment using statistical and machine learning techniques: a case study in the upper reaches of the Minjiang River, southwestern China. Front Earth Sci 10:986172. https://doi.org/10.3389/feart.2022.986172
Liu L, Yang C, Huang F, Wang XM (2021) Landslide susceptibility mapping by attentional factorization machines considering feature interactions. Geomat Nat Haz Risk 12:1837–1861
Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2001) Geographic information systems and science. Wiley, Chichester
Machane D, Bouhadad Y, Cheikhlounis G, Chatelain JL, Oubaiche EH, Abbes K, Guillier B, Bensalem R (2008) Examples of geomorphologic and geological hazards in Algeria. Nat Hazards 45:295–308
Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47:411–435
Maquaire O, Thiery Y, Malet J-P, Puissant A (2006) Evaluation et cartographie par SIG du risque ‘glissement de terrain’. Application aux alpes du sud. Interactions nature-societe - analyse et modeles. Umr cnrs 6554 letg, la baule
MATE/METL (1999) Plans de Prévention des Risques Naturels (PPR): Risques de Mouvements de Terrain. Ministère de l'Aménagement du Territoire et de l'Environnement, (MATE), Ministère de l'Equipement, des Transports et du Logement (METL). La Documentation Française, Paris
Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225
Montoya-Montes I, Rodríguez-Santalla I, Sánchez-García MJ, Alcántara-Carrió J, Martín-Velázquez S, Gómez-Ortiz D, Martín-Crespo T (2012) Mapping of landslide susceptibility of coastal cliffs: the Mont-Roig del Camp case study. Geologica Acta 10:4. https://doi.org/10.1344/105.000001776
Neuland H (1976) A prediction model of landslips. CATENA 3:215–230
Ozioko OH, Igwe O (2020) GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria. Environ Monit Assess 192 (2). https://doi.org/10.1007/S10661-019-7951-9
Petley DN, Bulmer MH, Murphy W (2002) Patterns of movement in rotational and translational landslides. Geology 30(8):719–722
Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Tran T-T, Bui DT (2019) Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175:203–218
Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression and artificial neural network models. Environ EarthSci 60:1037–1054
Rai DK, Xiong D, Zhao W et al (2022) An investigation of landslide susceptibility using logistic regression and statistical index methods in Dailekh District, Nepal. Chin Geogr Sci 32:834–851. https://doi.org/10.1007/s11769-022-1304-2
Raso E, Cevasco A, Di Martire D, Pepe G, Scarpellini P, Calcaterra D, Firpo M (2019) Landslide-inventory of the Cinque Terre National Park (Italy) and quantitative interaction with the trail network. J Maps 15(2):818–830. https://doi.org/10.1080/17445647.2019.1657511
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically based landslide susceptibility models. Earth Sci Rev 180:60–91
Rozos D, Pyrgiotis L, Skias S, Tsagaratos P (2008) An implementation of rock engineering system for ranking the instability Potential of natural slopes in Greek territory, In: An application in Karditsa County. Landslides 5:261–270
Sara F, Silvia B, Sandro M (2015) Landslide inventory updating by means of persistent scatterer interferometry (PSI): the Setta basin (Italy) case study. Geomatics Nat Hazards Risk 6(5–7):419–438
Schuster RL (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds.), Landslides – investigation and mitigation, National Res. Council, Washington, D.C., Transp Res Board Spec Report 247, p. 12–35
Sharma LP, Patel N, Ghose MK (2011) Debnath P (2011) Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators. Geocarto Int 26(6):491–504. https://doi.org/10.1080/10106049.2011.598951
Sharma A, Prakash C (2022) Predicting landslide susceptibility of a mountainous region using a hybrid machine learning-based model. In book: Environmental Concerns and Remediation, pp.191–209
Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. In: Turner AK, Schuster RL (eds) Landslides, investigation and mitigation. Transportation Research Board, National Research Council, Special Report 247. National Academy Press, Washington D.C., U.S.A, pp 129–177
Süzen ML (2002) Data driven landslide hazard assessment using geographical information systems and remote sensing. M.E.T.U. PhD Thesis, Unpublished 196
Thanh LN, FangYM CTY, Hoang TV, Nguyen QD, Lee CY, Wang C, Yin HY, Lin YC (2022) Using landslide statistical index technique for landslide susceptibility mapping: case study: Ban Khoang Commune, Lao Cai Province, Vietnam. Water 14:2814. https://doi.org/10.3390/w14182814
Van Westen CJ (2004) Geo-information tools for landslide risk assessment: an overview of recent developments. Landslides: evaluation and stabilization. Balkema, vol 1. Taylor & Francis Group, London, pp 39–56
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419
Van Westen CJ, Van Asch TW, Soeters R (2006) Landslide hazard and risk zonation-why is it still so difficult? Bull Eng Geol Env 65(2):167–184
Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO, Paris
Wang Y, Feng L, Li S, Ren F, Du Q (2020) A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China. CATENA 188:104425
Wang Q, Wang D, Huang Y, Wang Z, Zhang L, Guo Q, Chen W, Chen W, Sang M (2015) Landslide susceptibility mapping based on selected optimal combination of landslide predisposing factors in a large catchment. Sustainability 7(12):16653–16669. https://doi.org/10.3390/su71215839
Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. (Doctoral dissertation, TU Delft, Delft University of Technology), 1993
Xu Z, Che A, Cao Y, Zhang F (2021) Seismic landslide susceptibility assessment using principal component analysis and support vector machine. Res Square. https://doi.org/10.21203/rs.3.rs-761260/v1
Yelles-Chaouche A, Boudiaf A, Djellit H, Bracene (2020) La tectonique active de la région nord-algérienne. C. R. Geoscience 338: 126-139. https://doi.org/10.1016/j.crte.2005.11.002
Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12(14):2180
Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37
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This work was supported by the Directorate General for Scientific Research and Technological Development (DGRSDT), Algeria.
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Kab, A., Djerbal, L. & Bahar, R. Implementation of PCA multicollinearity method to landslide susceptibility assessment: the study case of Kabylia region. Arab J Geosci 16, 291 (2023). https://doi.org/10.1007/s12517-023-11374-5
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DOI: https://doi.org/10.1007/s12517-023-11374-5