Abstract
Geographical information systems (GIS) facilitate both current landslide mapping processes and the prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were created to reduce the damage of possible landslides in the Karaburun Peninsula of İzmir province. A landslide inventory map was produced from related databases in the first place, followed by the creation of parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers, and distance to fault lines). The frequency ratio (FR) method was utilized for producing the landslide susceptibility maps on a 5-level risk scale ranging from very low to very high-risk categories. Receiver operating characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 3% and 46% of the study area had high- and medium-risk categories, and the low landslide risk areas comprised 47% of the region. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been declared a special protection area and is a popular destination for tourism activities and energy facilities.
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References
AFAD (2015). Bütünleşik Tehlike Haritalarının Hazırlanması Heyelan ve Kaya Düşmesi Pratik Kılavuz. AFAD, Ankara
AFAD (2020) Afet Yönetimi Kapsamında 2019 Yılına Bakış ve Doğa Kaynaklı Olay İstatistikleri. https://www.afad.gov.tr/kurumlar/afad.gov.tr/e_Kutuphane/Kurumsal-Raporlar/Afet_Istatistikleri_2020_web.pdf. Accessed 10 June 2019
Akgün A, Dağ S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, 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
Alcantara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphol 47(2–4):107–124. https://doi.org/10.1016/S0169-555X(02)00083-1
Ataol M, Yeşilyurt S (2014) Çankırı-Ankara Karayolu Boyunca (Akyurt-Çankırı Arası) Heyelan Risk Bölgelerinin Belirlenmesi. Cograf Derg 29:51–69
Ayalew L, Yamagishi H (2005) The application og GIS-based logistic regression for landslide suspectibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphol 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Basharad M, Shah H (2016) Hameed N (2016) Lanslide suspectibility mapping using GIS and weighted overlay method: a case study from NW Himayalas, Pakistan. Arab J Geosci 9:292. https://doi.org/10.1007/s12517-016-2308-y
Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426. https://doi.org/10.1016/j.catena.2019.104426
Broeckx J, Vanmaercke M, Duchateau R, Poesen J (2018) A data-based landslide suspectibility map of Africa. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2018.05.002
Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016) A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in lanslide suspectibility mapping. Arab J Geosci 9:204. https://doi.org/10.1007/s12517-015-2150-7
Chen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014) Lanslide suspectibility mapping based on GIS and information value model for the Chencang District og Bajoi, China. Arab J Geosci 7:4499–4511. https://doi.org/10.10007/s12517-014-1369-z
Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833. https://doi.org/10.1016/j.catena.2020.104833
Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. CATENA 195:104777. https://doi.org/10.1016/j.catena.2020.104777
Çan T, Duman T, Olgun Ş, Çörekçioğlu Ş, Gülmez F, Elmacı H, Hamzaçebi S, Emre Ö (2013). Türkiye Heyelan Veri Tabanı. https://www.hkmo.org.tr/resimler/ekler/85a47f65233d5d0_ek.pdf. Accessed 20 June 2021
Demir G (2018) Coğrafi Bilgi Sistemleri ile Suşehri (Sivas) Heyelan Duyarlılık Analizi. GUSTIJ 8:96–112. https://doi.org/10.17714/gumusfenbil.299987
Ercanoğlu M, Hasekioğulları G, Günal B (2008) Heyelan Duyarlılığı Çalışmalarında Türkiye’nin Uluslararası Bilimsel Literatürdeki Yeri. Bull Eng Geol 26–27:35–51
Erener A, Lacasse S (2007) Landslide susceptibility mapping using GIS. In TMMOB Chamber of Survey and Cadastre Engineers National Geographic Information Systems Congress. Trabzon,Turkey.
Esendal Bozkurt N, Zontul M, Aslan Z (2018) Uydu Verilerine Dayalı Olarak Bitki Örtüsü Analizi. Aurum J Eng Syst Archit 2(1):75–82
Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321–347. https://doi.org/10.1080/13658816.2020.1808897
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27–8:861–874
Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227252
Gökçeoğlu C, Ercanoğlu M (2001) Heyelan Duyarlılık Haritalarının Hazırlanmasında Kullanılan Parametreleri İlişkin Belirsizlikler. Bull Earth Sci 23:189–206
Highland LM, Bobrowsky P (2008) The landslide handbook—a guide to understanding landslides https://pubs.usgs.gov/circ/1325/pdf/Sections/Section1.pdf. Accessed 20 June 2021
Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003
İZKA (2013) Urla-Çeşme-Karaburun Yarımadası Sürdürülebilir Kalkınma Stratejisi http://izka.org.tr/wp-content/uploads/pdf/14_yarimada_kalkinma_stratejisi.pdf . Accessed 20 June 2021
IsikPekkan O, Kurkcuoglu MAS, Cabuk SN, Aksoy T, Yilmazel B, Kucukpehlivan T, Dabanli A, Cetin M (2021) Assessing the effects of wind farms on soil organic carbon. Environ Sci Pollut Res 28:18216–18233. https://doi.org/10.1007/s11356-020-11777-x
Kalafatçıoğlu A (1961) A Geological Study in the Karaburun Peninsula. Bull Miner Res Explor 56(56):40–49
Reyhanlıoğlu Keçeoğlu Ç, Gelbal S, Doğan N (2016) ROC Eğrisi ile Kesme Puanının Belirlenmesi. J Soc Sci 50:553–562. https://doi.org/10.9761/JASSS3564
Kelarestaghi A, Ahmadi H (2009) Landslide suspectibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95–101. https://doi.org/10.1007/s12517-008-0022-0
Kirschbaum D, Kapnick SB, Stanley T, Pascale S (2020) Changes in extreme precipitation and landslides over High Mountain Asia. Geophys Res Lett 47:e2019GL085347. https://doi.org/10.1029/2019GL085347
Lacroix P, Dehecq A, Taipe E (2020) Irrigation-triggered landslides in a Peruvian desert caused by modern intensive farming. Nat Geosci 13(1):56–60. https://doi.org/10.1038/s41561-019-0500-x
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41. https://doi.org/10.1007/s10346-006-0047-y
Liu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, Lacasse S (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front 12(1):385–393. https://doi.org/10.1016/j.gsf.2020.04.014
Malet J, Nadim F (2012) Statistical modelling of Europa-wide landslide suspectibility using limited landslide inventory data. Landslides 9:357–369
Mas J, Filho B, Pontius R, Gutierrez M, Rodriques H (2013) A suite of tools for ROC analysis of spatial models. ISPRS Int J Geo-Inf 2:869–887. https://doi.org/10.3390/ijgi2030869
Mersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7:20. https://doi.org/10.1186/s40677-020-00155-x
Merghadi A, Yunus AP, Jie D, Jim W, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide suspectibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Nsengiyumva JB, Valentino R (2020) Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment. Geomat Nat Hazards Risk 11(1):1250–1277. https://doi.org/10.1080/19475705.2020.1785555
Özdemir 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. https://doi.org/10.1016/j.jseaes.2012.12.014
Pachauri A, 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
Pham BT, Dieu TB, Prakash I, Dholakia MB (2015) Landslide suspectibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency method. Int J Eng Res Technol 4(11):338–344. https://doi.org/10.17577/IJERTV4IS110285
Prefecture of Karaburun (2019) http://www.karaburun.gov.tr. Accessed 10 July 2019
Soyoung P, Chuluong C, Byungwoo K, Jinsoo K (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression and artificial neural network methods at the Inje Area, Korea. Env Earth Sci 68(5):1443–1464. https://doi.org/10.1007/s12665-012-1842-5
Pasang S, Kubíček P (2020) Landslide susceptibility mapping using statistical methods along the Asian Highway. Bhutan Geosci 10(11):430. https://doi.org/10.3390/geosciences10110430
Prakash N, Manconi A, Loew S (2020) Mapping landslides on EO data: performance of deep learning models vs. traditional machine learning models. Remote Sens 12(3):346. https://doi.org/10.3390/rs12030346
Sahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci 2(7):1–17. https://doi.org/10.1007/s42452-020-3060-1
Sarkar S, Kanungo DP (2017) GIS application in landslide susceptibility mapping of Indian Himalayas. In: Yamagishi H, Bhandary NP (eds) GIS Landslide. Springer, Tokyo, pp 211–219. https://doi.org/10.1007/978-4-431-54391-6_12
Silalahi FES, Pamela Arifianti Y, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci Lett 6:10. https://doi.org/10.1186/s40562-019-0140-4
Thanh DQ, Nguyen DH, Prakash I, Jaafari A, Nguyen VT, Van Phong T, Pham BT (2020) GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong, Vietnam Vietnam. J Earth Sci 42(1):55–56. https://doi.org/10.15625/0866-7187/42/1/14758
UNISDR (2018) Economic Losses, Poverty and Disasters: 1998–2017. https://www.preventionweb.net/files/61119_credeconomiclosses.pdf. Accessed 28 August 2021
Turan İ, Özkan B, Türkeş M, Dengiz O (2020). Landslide suspectibility for Black-Sea Region with spatial fuzzy multi-criteria decision analysis under semi-humid and humid terrestrial ecosystems. Theor Appl Climatol 140(18). https://doi.org/10.1007/s00704-020-03126-2
Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers Geosci 138:104445. https://doi.org/10.1016/j.cageo.2020.104445
Zhang YX, Lan HX, Li LP, Wu YM, Chen JH, Tian NM (2020) Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China. J Mt Sci 17(2):340–357. https://doi.org/10.1007/s11629-019-5702-6
Youssef AM, Al-Kathery M, Pradhan B (2015) Landslide suspectibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19:113–134. https://doi.org/10.1007/s12303-014-0032-8
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All authors contributed to the conception and design of the study. Muhittin Ozan Karaman performed conceptualization, data collection, analysis, and first draft writing stages. Saye Nihan Çabuk and Emrah Pekkan supervised and contributed to the context and method development, evaluation of the results, and final writing. All authors read and approved the final manuscript.
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Karaman, M.O., Çabuk, S.N. & Pekkan, E. Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey. Environ Sci Pollut Res 29, 91285–91305 (2022). https://doi.org/10.1007/s11356-022-21931-2
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DOI: https://doi.org/10.1007/s11356-022-21931-2