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Journals / Türk Coğrafya Dergisi / 2026 / Sayı: 85

Evaluation of slide and flowtype landslide susceptibility in the Aras River Basin via the logistic regression method

Aras Nehri havzasında lojistik regresyon yöntemiyle kayma ve akma tip heyelan duyarlılığı değerlendirmesi

Mehmet Emin Cihangir(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, FEN-EDEBİYAT FAKÜLTESİ)0000-0001-8881-5308
Pages
55–68
Publication Date
2024-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In general, the results obtained in the study indicate that (I) landslides in the basin are controlled to varying degrees by topographic and lithological factors depending on the type of landslide, (II) the high success of landslide susceptibility achieved by separately evaluating these factors for flows and slides, and (III) susceptibility maps created for different types of landslides are usable for regional planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Landslides, occurring due to slope instability, rank among the primary disasters in our country with the losses they cause.
📊
Key Findings & Data:One of the regions in Turkey where landslides are most concentrated and nested is the Aras river basin. 13% of this basin is prone to landslides, and reactivated landslides in the region ensure spatial persistence.
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Conclusion & Impact:In general, the results obtained in the study indicate that (I) landslides in the basin are controlled to varying degrees by topographic and lithological factors depending on the type of landslide, (II) the high success of landslide susceptibility achieved by separately evaluating these factors for flows and slides, and (III) susceptibility maps created for different types of landslides are usable for regional planning.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Aras nehri havzası · heyelan duyarlılığı · lojistik regresyon

Abstract

Landslides, occurring due to slope instability, rank among the primary disasters in our country with the losses they cause. One of the regions in Turkey where landslides are most concentrated and nested is the Aras river basin. 13% of this basin is prone to landslides, and reactivated landslides in the region ensure spatial persistence. This study aims to determine the landslide susceptibility of hillslopes in this area with high landslide density. For this purpose, the boundaries of the study area were first determined based on landslide density within the context of physical integrity. 3904 landslides were identified via Red Relief Image Maps and highresolution satellite imagery in the study area. These landslides were classified into two main types: flow and slide. Additionally, the basin vector has been divided into slope units. Flat areas (plains, valley basins, and structural flats) have been excluded from the slope units in the basin. Landslide content information has been entered into the slope units. The average and standard deviation values, such as slope, elevation, relief, slope profile, topographic wetness, and lithology, for each slope unit were used in landslide susceptibility assessment. Landslide susceptibility was determined using the logistic regression method. The landslide susceptibility of slope units was determined separately for flows and slides because the effects of factors controlling landslides and their degrees vary according to the type of landslide. An average AUC value of 0.79 was achieved for flows, while for slides, a success of 0.76 was obtained. In general, the results obtained in the study indicate that (I) landslides in the basin are controlled to varying degrees by topographic and lithological factors depending on the type of landslide, (II) the high success of landslide susceptibility achieved by separately evaluating these factors for flows and slides, and (III) susceptibility maps created for different types of landslides are usable for regional planning.

Özet

Yamaç stabilitesinin bozulmasıyla gerçekleşen heyelanlar, oluşturdukları kayıplarla ülkemizdeki başlıca afetler arasında yer almaktadır. Türkiye’de heyelanın en fazla yoğunlaştığı ve iç içe geliştiği bölgelerden biri de Aras Nehri Havzasıdır. Bu havzanın yüzde 13’ü heyelanlı olup, bölgede yeniden aktif heyelanlar mekânsal süreklilik sağlamaktadır. Bu çalışmanın amacı da yüksek heyelan yoğunluğuna sahip bu alanda, heyelan duyarlılığını belirlemektir. Bu amaç doğrultusunda ilk olarak fiziki bütünlük kapsamında heyelan yoğunluğuna göre çalışma alanının sınırları belirlenmiştir. Belirlenen çalışma alanında, kırmızı rölyef görüntü haritası ve yüksek çözünürlüklü uydu görüntüsü birlikteliği ile 3904 heyelan tespit edilmiştir. Bu heyelanlar akma ve kayma olarak iki ana tipte sınıflandırılmıştır. Ayrıca havza vektör tabanda yamaç ünitelerine ayrılmıştır. Havzada düzlük alanlar (ova, vadi tabanı ve yapısal düzlük) yamaç ünitelerinden çıkartılmıştır. Yamaç ünitelerine heyelan içerik bilgisi eklenmiştir. Her bir yamaç ünitesine ait eğim, yükselti, rölyef, yamaç profili, topoğrafik nemlilik gibi faktörlerin ortalama ve standart sapma değerlerinin yanı sıra litoloji bilgisi de yamaç duyarlılığında kullanılmıştır. Yamaçların duyarlılığı lojistik regresyon yöntemi ile tespit edilmiştir. Heyelanı kontrol eden faktör ve derecelerinin etkisi heyelan tipine göre değiştiğinden yamaç ünitelerinin heyelan duyarlılığı akma ve kaymalar için ayrı olarak belirlenmiştir. Ortalama AUC (doğru pozitif-yanlış pozitif) değerinde akmalarda 0,79, kaymalarda ise 0,76 başarı elde edilmiştir. Genel olarak çalışmada elde edilen sonuçlarda, (I) havzada heyelanın topoğrafik ve litolojik faktörler tarafından heyelan tipine göre farklı derecede kontrol edildiği (II) bu faktörlerin akma ve kayma için ayrı değerlendirilmesi ile oluşturulan heyelan duyarlılığının başarısının yüksek olduğu (III) farklı tip heyelan için oluşturulan duyarlılık haritaları bölgesel planlama için kullanıma sunulmuştur.

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Cite This Article

Cihangir, M. E. (2024). Evaluation of slide and flowtype landslide susceptibility in the Aras River Basin via the logistic regression method. Türk Coğrafya Dergisi, Sayı: 85, 55–68. https://doi.org/10.17211/tcd.1475065

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References (68)

  1. Alvioli, M., Guzzetti, F., & Marchesini, I. (2020). Parameter-free delineation of slope units and terrain subdivision of Italy. Geomorphology, 358, 107124. doi:https://doi.org/10.1016/j.geomorph.2020.107124
  2. Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., & Guzzetti, F. (2016). Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling. Geosci. Model Dev., 9(11), 3975-3991. doi:https://doi.org/10.5194/gmd-9-3975-2016
  3. Asadi, A., Baise, L. G., Koch, M., Moaveni, B., Chatterjee, S., & Aimaiti, Y. (2024). Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information. Natural Hazards, 120(6), 5163-5200. doi:https://doi.org/10.1007/s11069-023-06399-8
  4. Atkinson, P. M., & Massari, R. (1998). Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24(4), 373-385. doi:https://doi.org/10.1016/S0098-3004(97)00117-9
  5. 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), 15-31. doi:https://doi.org/10.1016/j.geomorph.2004.06.010
  6. Bai, S.-B., Wang, J., Lü, G.-N., Zhou, P.-G., Hou, S.-S., & Xu, S.-N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1-2), 23-31. doi:https://doi.org/10.1016/j.geomorph.2009.09.025
  7. Brabb, E. E., & Pampeyan, E. H. (1972). Preliminary map of landslide deposits in San Mateo County, California [Report] (Publication No. 344). Miscellaneous Field Studies Map, Issue. U.S.G. Survey. https://pubs.usgs.gov/publication/mf344
  8. Bui, D. T., Lofman, O., Revhaug, I., & Dick, O. (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59(3), 1413-1444. doi:https://doi.org/10.1007/s11069-011-9844-2
  9. Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427-445. doi:https://doi.org/10.1002/esp.3290160505
  10. Chiba, T., Kaneta, S.-i., & Suzuki, Y. (2008). Red relief image map: new visualization method for three dimensional data. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B2), 1071-1076.
  11. Chiba, T., Kaneta, S. I., & Ohashi, M. (2019). Digital Terrain Representation Methods and Red Relief Image Map, A New Visualization Approach. Proc. Int. Cartogr. Assoc., 2, 17. doi: https://doi.org/10.5194/ica-proc-2-17-2019
  12. Cihangir, M. E. (2022). Kayma tipi heyelanların farklı duyarlılık modellerinde kombinasyonu: Sakarya Havzası Yukarı Çığırı örneği. Türk Coğrafya Dergisi (80), 21-38. doi:https://doi.org/10.17211/tcd.1065523
  13. Cihangir, M. E., Görüm, T., & Nefeslioğlu, H. A. (2018). Heyelan tetikleyici faktörlerine bağlı mekânsal hassasiyet değerlendirmesi. [Spatial sensitivity assessment based on landslide trigger factors]. Türk Coğrafya Dergisi (70), 133-142. doi:https://doi.org/10.17211/tcd.410998
  14. Ciurleo, M., Calvello, M., & Cascini, L. (2016). Susceptibility zoning of shallow landslides in fine grained soils by statistical methods. Catena, 139, 250-264. doi:https://doi.org/10.1016/j.catena.2015.12.017
  15. Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2006). A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology, 50(7), 941-961. doi:https://doi.org/10.1007/s00254-006-0264-7
  16. Cruden, D. M., & Varnes, D. J. (1996). Landslides: investigation and mitigation. Chapter 3-Landslide types and processes. Transportation research board special report (247).
  17. Çan, T., Duman, T. Y., Olgun, Ş., Çörekçioğlu, Ş., Karakaya-Gülmez, F., Elmacı, H., Hamzaçebi, S., & Emre, Ö. (2013, Kasım, 1-13). Türkiye heyelan veri tabanı [Konferans sunum özeti]. TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara, Türkiye.https://obs.hkmo.org.tr/show-media/resimler/ekler/85a47f65233d5d0_ek.pdf
  18. Dagdelenler, G., Nefeslioglu, H. A., & Gokceoglu, C. (2016). Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bulletin of Engineering Geology and the Environment, 75(2), 575-590. doi:https://doi.org/10.1007/s10064-015-0759-0
  19. Dikau, R., Brunsden, D., Schrott, L., & Ibsen, M. L. (Eds.). (1997). Landslide recognition: Identification, movement, and causes. John Wiley & Sons.
  20. Duman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A., & Sonmez, H. (2006). Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51(2), 241-256. doi:https://doi.org/10.1007/s00254-006-0322-1
  21. Duman, T. Y., Olgun, Ş., Çan, T., Nefeslioğlu, H.A., Hamzaçebi, S., Elmacı, H., Durmaz S. ve Çörekçioğlu, Ş.,. (2009). Türkiye Heyelan Envanteri Haritası1:500.000 ölçekli Erzurum Paftası. Ankara, MTA.
  22. Elmaci, H. (2016). Ankara ili, Çubuk ve Kalecik ilçeleri ile Çankırı ili, Şabanözü ilçeleri arasının CBS tabanlı heyelan duyarlılık analizi [Yayınlanmamış yüksek lisans tezi]. Gazi Üniversitesi.
  23. Emre, Ö., Duman, T. Y., Özalp, S., Elmaci, H., Olgun, Ş., & Şaroğlu, F. (2013). Açıklamalı Türkiye Diri Fay Haritasi, Ölçek 1: 1.250.000. Maden Tetkik ve Arama Genel Müdürlüğü, Özel Yayın Serisi, 30, 89.
  24. Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology, 41(6), 720–730. doi:https://doi.org/10.1007/s00254-001-0454-2
  25. Falaschi, F., Giacomelli, F., Federici, P., Puccinelli, A., Avanzi, G. A., Pochini, A., & Ribolini, A. (2009). Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Natural Hazards, 50(3), 551-569. doi:https://doi.org/10.1007/s11069-009-9356-5
  26. Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94(3), 268-289. doi:https://doi.org/10.1016/j.geomorph.2006.09.023
  27. Gorsevski, P. V., Gessler, P., & Foltz, R. B. (2000, September, 2 - 8). Spatial prediction of landslide hazard using logistic regression and GIS [Conference presentation abstract]. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4), Banff, Alberta, Canada. https://www.researchgate.net/profile/Pece-Gorsevski/publication/313666224_Spatial_prediction_of_landslides_hazard_using_logistic_regression_and_GIS/links/615c6651c04f5909fd80792a/Spatial-prediction-of-landslides-hazard-using-logistic-regression-and-GIS.pdf
  28. Gorum, T., Gonencgil, B., Gokceoglu, C., & Nefeslioglu, H. A. (2008). Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Natural Hazards, 46(3), 323-351. doi:https://doi.org/10.1007/s11069-007-9190-6
  29. Gökceoglu, 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. Engineering Geology, 44(1-4), 147-161. doi:https://doi.org/10.1016/S0013-7952(97)81260-4
  30. Gökçe, O., Özden, Ş., & Demir, A. (2008). Türkiye’de afetlerin mekansal ve istatistiksel dağılımı afet bilgileri envanteri. Bayındırlık ve İskan Bakanlığı Afet İşleri Genel Müdürlüğü.
  31. Görüm, T. (2019). Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Engineering Geology, 258, 105155. doi:https://doi.org/10.1016/j.enggeo.2019.105155
  32. Guzzetti, F. (2005). Landslide Hazard and Risk Assesment [Unpublished doctoral dissertation]. University of Bonn.
  33. Guzzetti, F., Ardizzone, F., Cardinali, M., Galli, M., Reichenbach, P., & Rossi, M. (2008). Distribution of landslides in the Upper Tiber River basin, central Italy. Geomorphology, 96(1), 105-122. doi:https://doi.org/10.1016/j.geomorph.2007.07.015
  34. Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K.-T. (2012). Landslide inventory maps: New tools for an old problem. EarthScience Reviews, 112(1), 42-66. doi:https://doi.org/10.1016/j.earscirev.2012.02.001
  35. Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1), 166-184. doi:https://doi.org/10.1016/j.geomorph.2006.04.007 Hatchinson, J. (1988, September, 1). Morphological and geotechnical parameters of landslides in relation to geology and hydrology [Conference presentation abstract]. Proc.1st. Int. Symp. on Landslides. Lausanne, Switzerland. https://www.sciepub.com/reference/209037
  36. Hay, W. W., DeConto, R. M., & Wold, C. N. (1997). Climate: Is the past the key to the future? Geologische Rundschau, 86(2), 471-491. doi:https://doi.org/10.1007/s005310050155
  37. Hutton, J. (1899). Theory of the earth: With proofs and illustrations (Vol. 111). Geological society. Kirschbaum, D. B., Adler, R., Hong, Y., & Lerner-Lam, A. (2009). Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories. Nat. Hazards Earth Syst. Sci., 9(3), 673-686. doi:https://doi.org/10.5194/nhess-9-673-2009
  38. Lee, S., Choi, J., & Min, K. (2004). Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing, 25(11), 2037-2052. doi:https://doi.org/10.1080/01431160310001618734
  39. Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095-1113. doi:https://doi.org/10.1007/s002540100310
  40. Lee, S., Ryu, J. H., Min, K., & Won, J. S. (2003). Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 28(12), 1361-1376. doi:https://doi.org/10.1002/esp.593
  41. Lei, T., Zhang, Y., Lv, Z., Li, S., Liu, S., & Nandi, A. K. (2019). Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 16(6), 982-986. doi:https://doi.org/10.1109/LGRS.2018.2889307
  42. Liu, X., Zhao, C., Yin, Y., Tomás, R., Zhang, J., Zhang, Q., Wei, Y., Wang, M., & Lopez-Sanchez, J. M. (2024). Refined InSAR method for mapping and classification of active landslides in a high mountain region: Deqin County, southern Tibet Plateau, China. Remote Sensing of Environment, 304, 114030. doi:https://doi.org/10.1016/j.rse.2024.114030
  43. Loche, M., Alvioli, M., Marchesini, I., Bakka, H., & Lombardo, L. (2022). Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory. EarthScience Reviews, 232, 104125. doi:https://doi.org/10.1016/j.earscirev.2022.104125
  44. Lombardo, L., Cama, M., Conoscenti, C., Märker, M., & Rotigliano, E. (2015). Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Natural Hazards, 79(3), 1621-1648. doi:https://doi.org/10.1007/s11069-015-1915-3
  45. Lombardo, L., & Mai, P. M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244, 14-24. doi:https://doi.org/10.1016/j.enggeo.2018.07.019
  46. Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. Earth Surface Processes and Landforms, 29(6), 687-711. doi:https://doi.org/10.1002/esp.1064
  47. Nandi, A., & Shakoor, A. (2010). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110(1), 11-20. doi:https://doi.org/10.1016/j.enggeo.2009.10.001
  48. Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, 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. Engineering Geology, 97(3), 171-191. doi:https://doi.org/10.1016/j.enggeo.2008.01.004
  49. Okay, A. I. (2008). Geology of Turkey: a synopsis. Anschnitt, 21, 19-42.
  50. Özpolat, E., Yıldırım, C., & Görüm, T. (2020). The Quaternary landforms of the Büyük Menderes Graben System: the southern Menderes Massif, western Anatolia, Turkey. Journal of Maps, 16(2), 405–419. https://doi.org/10.1080/17445647.2020.1764874
  51. Pachauri, A., & Pant, M. (1992). Landslide hazard mapping based on geological attributes. Engineering Geology, 32(1-2), 81-100. doi:https://doi.org/10.1016/0013-7952(92)90020-Y
  52. Pašek, J. (1975). Landslides inventory. Bulletin of Engineering Geology & the Environment, 12(1), 73-74.
  53. Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. EarthScience Reviews, 180, 60-91. doi:https://doi.org/10.1016/j.earscirev.2018.03.001
  54. Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., & Rossi, M. (2017). Characterization and quantification of path dependency in landslide susceptibility. Geomorphology, 292, 16-24. doi:https://doi.org/10.1016/j.geomorph.2017.04.039
  55. Santacana, N., Baeza, B., Corominas, J., De Paz, A., & Marturiá, J. (2003). A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet area (Eastern Pyrenees, Spain). Natural Hazards, 30(3), 281-295. doi:https://doi.org/10.1023/B:NHAZ.0000007169.28860.80
  56. Süzen, M. L., & Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology, 71(3), 303-321. doi:https://doi.org/10.1016/S0013-7952(03)00143-1
  57. Trigila, A., Iadanza, C., & Spizzichino, D. (2010). Quality assessment of the Italian Landslide Inventory using GIS processing. Landslides, 7(4), 455-470. doi:https://doi.org/10.1007/s10346-010-0213-0
  58. Üzel, G. N. (2019). Van ili heyelan duyarlılığının lojistik regresyon analizi ve frekans oranı yöntemiyle incelenmesi [Yayınlanmamış yüksek lisans tezi]. Ondokuz Mayıs Üniversitesi.
  59. Van Den Eeckhaut, M., Hervás, J., Jaedicke, C., Malet, J. P., Montanarella, L., & Nadim, F. (2012). Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides, 9(3), 357-369. doi:https://doi.org/10.1007/s10346-011-0299-z
  60. Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G., & Vandekerckhove, L. (2006). Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology, 76(3), 392-410. doi:https://doi.org/10.1016/j.geomorph.2005.12.003
  61. Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3), 112-131. doi:https://doi.org/10.1016/j.enggeo.2008.03.010
  62. Varnes, D. J. (1978). Slope movement types and processes. Special report, 176, 11-33.
  63. Yıldırım, N., & Parlak, O. (2008). Tekman-Pasinler (Erzurum) arasında yüzeyleyen ofiyolitik birimlerin jeolojisi ve petrografik özellikleri. Çukurova Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 18(2), 35-45.
  64. Yılmaz, A., Terlemez, İ., & Uysal, Ş. (1988). 1: 100 000 ölçekli Türkiye Jeoloji Haritaları Serisi Erzurumİ 47 paftası. Ankara, Maden Tetkik ve Arama Genel Müdürlüğü.
  65. Wieczorek, G. F. (1984). Preparing a Detailed Landslide-Inventory Map for Hazard Evaluation and Reduction. Environmental & Engineering Geoscience, 21(3), 337-342. doi:https://doi.org/10.2113/gseegeosci.xxi.3.337
  66. WP/WLI. (1994). A suggested method for describing the causes of a landslide. Bull Int Assoc Eng Geol, 50, 71-74.
  67. Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1), 166-184. doi:https://doi.org/10.1016/j.geomorph.2006.04.007 Hatchinson, J. (1988, September, 1). Morphological and geotechnical parameters of landslides in relation to geology and hydrology [Conference presentation abstract]. Proc.1st. Int. Symp. on Landslides. Lausanne, Switzerland. https://www.sciepub.com/reference/209037
  68. Yılmaz, A., Terlemez, İ., & Uysal, Ş. (1988). 1: 100 000 ölçekli Türkiye Jeoloji Haritaları Serisi Erzurumİ 47 paftası. Ankara, Maden Tetkik ve Arama Genel Müdürlüğü.