Abstract
The aim of the present study was to produce an earthflow susceptibility map for the city center and environs of the province of Rize located at the Northeastern part of Turkey. The study area is the rainiest region of Turkey, and due to the triggering effect of precipitation earthflows are frequently observed in and around the study area. Besides this point, weathered rock units and steep topography accompany with precipitation for the occurrence of earthflow cases. Considering this point, an earthflow susceptibility mapping was inferred to be a necessity for the area. Parameters, such as lithology, slope gradient, slope aspect, topographical wetness index (TWI), stream power index (SPI), slope curvature, and sediment capacity index (LS) were considered to be earthflow conditioning parameters. A multi-temporal earthflow inventory map for a period of 4 years (2011–2015) was initially generated by way of remote sensing approach and field surveys. Earthflow susceptibility map was produced using the logistic regression method after which the produced susceptibility map was validated. The mapped earthflows were separated into two groups prior to modelling and validation. The first group was for training and the second group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of mapped earthflows. Area under curvature (AUC) approach was applied for validation purposes. The prediction capability of the earthflow susceptibility map produced can be regarded as acceptable in accordance with the AUC values of 0.62 for the logistic regression model. Based on these results, the obtained earthflow susceptibility map can be used to mitigate hazards related to landslides and to aid in land-use planning for the Rize city center.
Similar content being viewed by others
References
Akgün A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multicriteria decision and likelihood ratio methods: case study at Izmir, Turkey. Landslides 9(1):93–106. https://doi.org/10.1007/s10346-011-0283-7
Akgün A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51(8):1377–1387. https://doi.org/10.1007/s00254-006-0435-6
Akgün A, Erkan O (2016) Landslide susceptibility mapping by geographical information systems-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arab J Geosci 9(165):1–15. https://doi.org/10.1007/s12517-015-2142-7
Akgün A, Dağ S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Fındıklı, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143. https://doi.org/10.1007/s00254-007-0882-8
Akgün A, Sezer EA, Nefeslioğlu HA, Gökceoğlu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34. https://doi.org/10.1016/j.cageo.2011.04.012
Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003
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(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Landf 26:1251–1263. https://doi.org/10.1002/esp.263
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrolol Sci Bull 24:43–69. https://doi.org/10.1080/02626667909491834
Çan T, Nefeslioğlu HA, Gökceoğlu C, Sönmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology 72(1–4):250–271. https://doi.org/10.1016/j.geomorph.2005.05.011
Carrara A, Cardinali M, Guzetti F, Reichenbach P (1995) GIS-based techniques for mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht, pp 135–176
Carrara A, Cardinali M, Guzzetti F, Reinhenbach P (1999) Use of GIS technology in the prediction and monitoring of landslide hazard. Nat Hazards 20(2–3):117–135. https://doi.org/10.1023/A:1008097111310
Castellanos Abella EA, Van Westen CJ (2008) Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantanamo, Cuba. Geomorphology 94(3–4):453–466. https://doi.org/10.1016/j.geomorph.2006.10.038
Çevik 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. https://doi.org/10.1007/s00254-003-0838-6
Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411. https://doi.org/10.1007/s10064-006-0064-z
Chen W, Shahabi H, Shirzadi A, Hong H, Akgün A, Tian Y, Liu J, Zhu AX, Li S (2018) Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bull Eng Geol Environ 78:4397–4419. https://doi.org/10.1007/s10064-018-1401-8
Çölkesen İ, Şahin EK, Kavzoğlu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64. https://doi.org/10.1016/j.jafrearsci.2016.02.019
Cruden DM, Varnes DJ (1996) Landslide types and processes, transportation research board, U.S. National Academy of Sciences, Special Report, vol 247, pp 36–75
Dağ, S (2007) Landslide susceptibility analysis of Çayeli (Rize) and its vicinity by statistical methods, (PhD Thesis), Karadeniz Technical University (unpublished)
Dağ S, Bulut F (2012) An example for preparation of GIS-based landslide susceptibility maps: Çayeli (Rize, NE Türkiye). J Geol Eng 36(1):35–62 (in Turkish)
Dağ S, Bulut F, Alemdağ S, Kaya A (2011) A general assesment for methods and parameters used in production of landslide susceptibility maps. Gümüşhane Univ J Sci Technol Inst 1(2):151–176 (in Turkish)
Dağdelenler G, Nefeslioğlu HA, Gökçeoğlu C (2015) Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bull Eng Geol Environ 75:575–590. https://doi.org/10.1007/s10064-015-0759-0
Dai FC, Lee CF, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391. https://doi.org/10.1007/s002540000163
Demir G, Aytekin M, Akgün A, İkizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the north Anatolian fault zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65(3):1481–1506. https://doi.org/10.1007/s11069-012-0418-8
Donati L, Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the apennines (Valnerina; Perugia, Italy). Eng Geol 63(3–4):277–289. https://doi.org/10.1016/S0013-7952(01)00087-4
Duman TY, Nefeslioglu HA, Çan T, Olgun Ş, Durmaz S, Hamzaçebi S (2007) 1:500.000 Scale landslide ınventory map of Turkey, Trabzon Sheet, MTA, Ankara (unpublished report)
Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞHB, Akgün H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29(1):132–158. https://doi.org/10.1080/13658816.2014.953164
Ercanoğlu M, Gökçeoğlu C (2002) Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730. https://doi.org/10.1007/s00254-001-0454-2
Erener A, Mutlu A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55. https://doi.org/10.1016/j.enggeo.2015.09.007
Fotheringham AS, Oshan TM (2016) Geographically weighted regression and multicollinearity: dispelling the myth. J Geogr Syst 18:303–329. https://doi.org/10.1007/s10109-016-0239-5
General Directory of Meteorological Services of Turkey (2019) https://www.meteor.gov.tr/. Accessed 15 Feb 2020.
Gökçeoğlu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen Region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44(1):147–161. https://doi.org/10.1016/S0013-7952(97)81260-4
Gökçeoğlu C, Ercanoğlu M (2001) Uncertainties on the parameters employed in preparation of landslide susceptibility maps. Bull Earth Sci Appl Res Centre Hacettepe Univ 23:189–206 (in Turkish)
Gökçeoğlu C, Nefeslioğlu HA, Sönmez H et al (2005) Regional and medium scale landslide susceptibility, hazard and risk assessment. In: 1st Landslide symposium, Trabzon, Proceedings book, pp 9–23
Gorsevski PV, Gessler P, Foltz RB (2000) Spatial prediction of landslide hazard using logistic regression and GIS. In: 4th International conference on ıntegrating GIS and environmental modelling, Alberta, Canada, p 9
Görüm T (2019) Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Eng Geol 258:105–155. https://doi.org/10.1016/j.enggeo.2019.105155
Görüm T, Gönençgil B, Gökçeoğlu C, Nefeslioğlu HA (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Nat Hazards 46:323–351. https://doi.org/10.1007/s11069-007-9190-6
Güven İH (1993) 1:250000-Scaled geology and compilation of the Eastern Pontide. General Directorate of Mineral Research and Exploration (MTA) of Turkey, Ankara (unpublished)
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
Hong H, Pradhan B, Jebur MN, Bui DT, Xu C, Akgün A (2016) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75:40. https://doi.org/10.1007/s12665-015-4866-9
Ildır B (1995) The distribution of lanslides and disasters law with ralated applications in Turkey. In: Önalp A (ed) Proceeding of 2nd national landslide symposium of Turkey, Sakarya University, pp 1–9 (in Turkish)
Ismail EH, Rogers JD, Ahmed MF, Usery EL, Abdelsalam MG (2018) Landslide susceptibility mapping of Blue Nile and Tekeze River Basins using oblique rainfall-aspect rasters. Bull Eng Geol Environ 77(4):1311–1329. https://doi.org/10.1007/s10064-017-1033-4
Kavzoğlu T, Şahin EK, Çolkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines and logistic regression. Landslides 11:425–439. https://doi.org/10.1007/s10346-013-0391-7
Kayastha P, Dhital MR, Smedt FD (2013) Evaluation of the consistency of landslide susceptibility mapping: a case study from the Kankai watershed in east Nepal. Landslides 10:785–799. https://doi.org/10.1007/s10346-012-0361-5
Kıncal C, Akgün A, Koca MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59:745–756. https://doi.org/10.1007/s12665-009-0070-0
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491. https://doi.org/10.1080/01431160412331331012
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113. https://doi.org/10.1007/s002540100310
Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verication using the bayesian probability model. Environ Geol 43(1–2):120–131. https://doi.org/10.1007/s00254-002-0616-x
Maharajjar R (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica, West Indies. Eng Geol 34(1–2):53–79. https://doi.org/10.1016/0013-7952(93)90043-C
Melchiorre C, Abella EC, Van Westen CJ, Matteucci M (2011) Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guanta´namo, Cuba. Comput Geosci 37(4):410–425. https://doi.org/10.1016/j.cageo.2010.10.004
Menard S (1995) Applied lojistic regression analysis. Sage University Paper Series on Quantitative Application in Social Sciences, Thousand Oaks, California, 106, 98
Moore ID, Burch GJ (1986) Physical basis of the length slope factor in the universal soil loss equation. Soil Sci Soc Am 50(5):1294–1298
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30
Nagarajan R, Roy A, Vidon Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58(4):275–287
Nefeslioğlu HA, Gökçeoğlu C (2011) Probabilistic risk assessment in medium scale for rainfall-ınduced earthflows: Çatakli catchment area (Çayeli, Rize, Turkey). Math Probl Eng. https://doi.org/10.1155/2011/280431
Nefeslioğlu HA, Gokceoğlu C, Sönmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191. https://doi.org/10.1016/j.enggeo.2008.01.004
Nefeslioğlu HA, Sezer E, Gökceoğlu C, Bozkır AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng. https://doi.org/10.1155/2010/901095
Nefeslioğlu H, Gökçeoğlu C, Sönmez H, Görüm T (2011) Medium-scale hazard mapping for shallow landslide initiation: the Büyükköy catchment area (Çayeli, Rize, Turkey). Landslides 8(4):459–483. https://doi.org/10.1007/s10346-011-0267-7
Nefeslioğlu AH, Sezer EA, Gökceoğlu C, Ayaş Z (2013) A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput Geosci 59:1–8. https://doi.org/10.1016/j.cageo.2013.05.010
Neuhauser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model. Landslides 9:511–528. https://doi.org/10.1007/s10346-011-0305-5
NIK Construction Trade Ltd. Co. (2015) https://www.nik.com.tr/. Accessed 01 Jan 2016
O'brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690. https://doi.org/10.1007/s11135-006-9018-6
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3
Osna T, Sezer EA, Akgün A (2014) Geofis: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30. https://doi.org/10.1016/j.cageo.2013.12.016
Özdamar K (2002) Statistical data analysis with package programs (multivariate analyzes)1–2, 4th edn. Kaan Kitapevi, Eskişehir (in Turkish)
Özer BC, Mutlu B, Nefeslioğlu HA, Sezer EA, Rouai M, Dekayir A, Gökçeoğlu C (2020) On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco). Bull Eng Geol Environ 79(1):551–568. https://doi.org/10.1007/s10064-019-01585-0
Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32(1–2):81–100. https://doi.org/10.1016/0013-7952(92)90020-Y
Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logisticregression, analytical hierarchy process, and statisticalindex models and assessment of their performances. Nat Hazards 69:749–779. https://doi.org/10.1007/s11069-013-0728-5
Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349. https://doi.org/10.1007/s12665-010-0705-1
Pradhan B, Sezer EA, Gökceoğlu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177. https://doi.org/10.1109/TGRS.2010.2050328
Reichenbach P, Rossi M, Malamud BD, Mihir M (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Romer C, Ferentinou M (2016) Shallow landslide susceptibility assessment in a semiarid environment-a quaternary catchment of KwaZulu-Natal, South Africa. Eng Geol 201:29–44. https://doi.org/10.1016/j.enggeo.2015.12.013
Roodposhti MS, Rahimi S, Beglou MJ (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95. https://doi.org/10.1007/s11069-012-0523-8
Santacana N, Baeza B, Corominas J, Paz AD, Marturia J (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility maping in La Pobla de Lillet area (Eastern Pyrenees, Spain). Nat Hazards 30(3):281–295. https://doi.org/10.1023/B:NHAZ.0000007169.28860.80
Segoni S, Pappafico G, Luti T, Catani F (2020) Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization. Landslides. https://doi.org/10.1007/s10346-019-01340-2
Sevgen E, Kocaman S, Nefeslioğlu HA, Gökceoğlu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression. ANN Random For Sens 19(18):3940. https://doi.org/10.3390/s19183940
Sezer EA, Pradhan B, Gökçeoğlu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219. https://doi.org/10.1016/j.eswa.2010.12.167
Sezer EA, Nefeslioglu HA, Osna T (2017) An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software. Comput Geosci 98:26–37. https://doi.org/10.1016/j.cageo.2016.10.001
Süzen ML, Doyuran V (2003) A comparison of the GIS based landslide susceptibility assesment methods: multivariate versus bivariate. Environ Geol 45(5):665–679. https://doi.org/10.1007/s00254-003-0917-8
Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Eng Geol 71:303–321. https://doi.org/10.1016/S0013-7952(03)00143-1
Thiebes B (2011) Landslide analysis and early warning-local and regional case study in the Swabian Alb. PhD thesis, University of Vienna
Van Westen CJ, Bonilla JBA (1990) Mountain hazard analysis using a PC-based GIS. In: Proceeding of the 6th ınternational congress of engineering geology, August Amsterdam, Netherlands, D.G. Price (ed.), Balkema, pp 265–271
Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993. https://doi.org/10.1016/j.scitotenv.2019.02.263
Wilson JP, Gallant JC (2000) Terrain analysis principles and application. John Wiley, Hoboken
Yanar T, Kocaman S, Gökçeoğlu C (2020) Use of mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS Int J Geoinf 9(2):114. https://doi.org/10.3390/ijgi9020114
Yange L, Liu X, Han Z, Dou J (2020) Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: a case study of Qingchuan area, China. Appl Sci 10(1107):1–16. https://doi.org/10.3390/app10031107
Yeşilnacar 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
Yılmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural networks (ANN). Environ Earth Sci 60(3):505–519. https://doi.org/10.1007/s12665-009-0191-5
Acknowledgements
The authors would like to acknowledge the Recep Tayyip Erdogan University for funding this work through research project no: 2014.109.01.01.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dağ, S., Akgün, A., Kaya, A. et al. Medium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey. Environ Earth Sci 79, 468 (2020). https://doi.org/10.1007/s12665-020-09217-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-020-09217-7