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Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern Algeria

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Abstract

Landslides are one of the most common and damaging geological hazards that constrain the urban planning and development of many cities in northern Algeria. Therefore, landslide susceptibility maps (LSMs) constitute an essential tool for effective hazard management and long-term development planning in landslide-prone areas. The aim of this work is to prepare and compare the LSMs by applying GIS-based statistical and machine learning models for the new city of Sidi Abdellah and surrounding areas (Northern Algeria). We implemented the statistical models of the frequency ratio (FR), statistical index (SI), and weights of evidence (WoE) models, and the machine learning models represented by a logistic regression (LR) model for landslide susceptibility prediction. An historical landslide inventory map was produced using the interpretation of Google Earth satellite images, available historical records, and geological field investigations. The obtained landslides were randomly divided into the training (70%) and validation (30%) processes. Furthermore, 12 influencing factors for landslide occurrence (including precipitation, slope, elevation, distance to drainage, aspect, land use, density of streams, distance to road, lithology, distance to fault, seismicity, and density of roads) were selected to prepare thematic maps and were considered for susceptibility analysis. Subsequently, landslide susceptibility assessment and mapping are performed by considering the inventoried landslide events and their related predisposing factors using LR, SI, WoE, and FR models in GIS. The accuracy of the four models was verified, validated, and compared using the area under curve (AUC) value of the Receiver Operating Characteristics Curves (ROC) method. The validation results showed that all the used statistical models provided a good accuracy in predicting landslide susceptibility than the machine learning models, with the SI model having a higher AUC value of 80.1% than the WoE (78.2%), FR (78.2%), and LR (64.2%) models. Based on these results, we conclude that the established maps can be used as useful tools for land use planning and risk reduction in the urban area of Sidi Abdellah.

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The corresponding author will provide data to back up the study’s findings upon a reasonable request.

References

  • ANRH, Agence Nationale des Ressources Hydrauliques (2005) Precipitation map performed from precipitation database from local meteorological stations located in the vicinity of the study area by using Kriging interpolation

  • Aymé A (1954) Service de la Carte Géologique de l'Algérie, Étude des terrains néogènes de la cluse du Mazafran:(Sahel d'Alger)

  • Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (eds) Statistical applications in earth sciences. Geological Survey of Canada, Ottawa, pp 171–183

    Google Scholar 

  • Bourenane H, Guettouche MS, Bouhadad Y, Braham M (2016) Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weights factor, logistic regression, weights of evidence, and analytical hierarchy process methods. Arab J Geosci 9:154. https://doi.org/10.1007/s12517-015-2222-8

    Article  Google Scholar 

  • CGS (2009) Centre National de Recherche Appliquée en Génie Parasismique. Seismic Microzonation study of the city of Sidi Abdellah. Unpublished Internal Report.

  • Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Djerbal L, Khoudi I, Alimrina N, Melbouci B, Bahar R (2017) Assessment and mapping of earthquake-induced landslides in Tigzirt City, Algeria. Nat Hazards 87:1859–1879. https://doi.org/10.1007/s11069-017-2831-5

    Article  Google Scholar 

  • Goetz JN, Brenning A, Petschko H (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007

    Article  Google Scholar 

  • Guirous L, Dubois L, Melbouci B (2014) Contribution à l’étude du mouvement de terrain de la ville de Tigzirt (Algérie). Bull Eng Geol Environ. https://doi.org/10.1007/s10064-014-0624-6

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216. https://doi.org/10.1016/S0169-555X(99)00078-1

    Article  Google Scholar 

  • Hadji R, Boumazbeur A, Limani Y, Baghem M, Chouabi A, Demdoum A (2013) Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quatern Int 302:224–237. https://doi.org/10.1016/j.quaint.2012.11.027

    Article  Google Scholar 

  • Hallal N, Dubois L, Bougdal R, Djouder F (2017) Instabilités gravitaires dans la région de Béjaïa (Algérie): Inventaire et appréciation de l’importance relative des différents paramètres conduisant au déclenchement, au maintien ou à l’activation des instabilités. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1050-3

    Article  Google Scholar 

  • Harbi A, Maouche S, Ayadi A, Benouar D, Panzag F, Benhallou H (2004) Seismicity and tectonic structures in the site of algiers and its surroundings: a step towards microzonation. Pure Appl Geophys 161(2004):949–967. https://doi.org/10.1007/s00024-003-2502-1

    Article  Google Scholar 

  • Huang F, Cao Z, Guo J, Jian SH, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580

    Article  Google Scholar 

  • Huang J, Wu X, Ling S, Li X, Wu Y, Peng L, He Z (2022) A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020. Environ Sci Pollut Res 29:86954–86993. https://doi.org/10.1007/s11356-022-23732-z

    Article  Google Scholar 

  • Karim Z, Hadji R, Hamed Y (2019) GIS based approaches for the landslide susceptibility prediction in Setif Region (NE Algeria). Geotech Geol Eng 37(1):359–374. https://doi.org/10.1007/s10706-018-0615-7

    Article  Google Scholar 

  • Koica (2003) Master Plan project of the city of Sidi Abdellah established by KOREA International Cooperation Agency (KOICA). Unpublished Internal Report

  • Lani J (2007) Generalized linear models. Organ. Res. Methods 7:127-150

    Google Scholar 

  • Laribi A, Walstra J, Ougrine M, Seridi A, Dechemi N (2014) Use of digital photogrammetry for the study of unstable slopes in urban areas: Case study of the El Biar landslide. Algiers Eng Geol 187(2015):73–83. https://doi.org/10.1016/j.enggeo.2014.12.018

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Eng Geol 40:1095–1113. https://doi.org/10.1007/s002540100310

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y

    Article  Google Scholar 

  • Meghraoui M (1988) Géologie des zones sismiques du Nord de l’Algérie : Paleosismologie, tectonique active et synthèse sismotectonique. Thèse de Doctorat, spécialité Sciences Naturelles, mention Géologie Structurale, Université de Paris Sud, Centre Orsay N°.3495 Paris le 23 juin 1988.

  • Merghadia A, Yunusb AP, Douc JD, Whiteleye J, Thai Phamg B, Tien Buihn B, Avtari R, Boumezbeur A (2020) Machine learning methods for landslide susceptibility studies : A comparative overview of algorithm performance. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103225

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of evidence models. J Asian Earth Sci 61:221–236. https://doi.org/10.1016/j.jseaes.2012.10.005

    Article  Google Scholar 

  • Moulouel H, Bouchelouh A, Bensalem R, Tebbouche MY, Ait Benamar D, Oubaiche E, Gharbi S, Machane D, Benamghar A (2020) The Mahelma fault: a secondary structure of the Sahel anticline? Arab J Geosci 13:715. https://doi.org/10.1007/s12517-020-05694-z

    Article  Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71:523–547. https://doi.org/10.1007/s11069-013-0932-3

    Article  Google Scholar 

  • Ozdemir A, Altural T (2012) 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 2:2. https://doi.org/10.1016/j.jseaes.2012.12.014

    Article  Google Scholar 

  • Park S, Choi C, Kim B (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 68:1443–1464. https://doi.org/10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779. https://doi.org/10.1007/s11069-013-0728-5

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759. https://doi.org/10.1016/j.envsoft.2009.10.016

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30. https://doi.org/10.1007/s10346-009-0183-2

    Article  Google Scholar 

  • Pradhan B, Youssef A (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326. https://doi.org/10.1007/s12517-009-0089-2

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Pradhan B, Pourghasemi HR, Khumamoto T, Akgun A (2014) Application of frequency ratio, statistical index and weights-of-evidence models, and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742. https://doi.org/10.1007/s12517-012-0807-z

    Article  Google Scholar 

  • Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66

    Article  Google Scholar 

  • Sahana M, Pham BT, Shukla M, Costache R, Thu D, Chakrabortty R, Satyam N, Nguyen HD, Phong TV, Le HV, Pal SC, Areendran G, Imdad K, Prakash I (2020) Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocarto Int 37:2747–2771. https://doi.org/10.1080/10106049.2020.1837262

    Article  Google Scholar 

  • Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92(1–2):38–59. https://doi.org/10.1016/j.geomorph.2007.02.020

    Article  Google Scholar 

  • Tien Bui D, 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

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement, types and processes. In: Schuster RL, Krizek RJ (Eds) Landslides, analyses and control. National Academy of Science, Report 176, Washington, DC, pp 11–35.

  • Van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. ITC publication, Enschede, The Netherlands, pp 73–84.

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

    Article  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. https://doi.org/10.1016/j.catena.2007.01.003

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266. https://doi.org/10.1016/j.enggeo.2005.02.002

    Article  Google Scholar 

  • Yılmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  • Zhou G, Esaki T, Mitani Y, Xie M, Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386

    Article  Google Scholar 

  • Zhou S, Zhang Y, Tan X, Abbas SM (2021) A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China. Arab J Geosci 14:440. https://doi.org/10.1007/s12517-021-06630-5

    Article  Google Scholar 

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Acknowledgements

This research is supported by the National Center for Applied Research in Earthquake Engineering (CGS). We thank everyone who provided assistance in the process of conducting this research. We are grateful to the local authorities and residents of Sidi Abdallah municipality for their invaluable assistance in the field. The authors are grateful to the Editor-in-Chief of the Modeling Earth Systems and Environment (MESE) journal and to the anonymous reviewer for their valuable time and comments, which led to the improvement of an early version of this manuscript.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Division Microzonage Sismique, Centre National de Recherche Appliquée en Génie Parasismique (CGS), Algiers, Algeria

    Bourenane Hamid, Braham Massinissa & Guessoum Nabila

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  1. Bourenane Hamid
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Correspondence to Bourenane Hamid.

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Hamid, B., Massinissa, B. & Nabila, G. Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern Algeria. Model. Earth Syst. Environ. 9, 2477–2500 (2023). https://doi.org/10.1007/s40808-022-01633-x

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