VAN İLİ HEYELAN DUYARLILIĞININ FREKANS ORANI YÖNTEMİYLE ANALİZİ

Year 2021, Volume: 26 Issue: 3, 865 - 884, 31.12.2021

Nergiz Üzel Derya Öztürk

https://doi.org/10.17482/uumfd.969246

Cited By: 3

Abstract

Bu çalışmada, Van ili heyelan duyarlılığı Coğrafi Bilgi Sistemleri ortamında Frekans Oranı yöntemi kullanılarak belirlenmiştir. Heyelan duyarlılık analizinde; litoloji, fay hatlarına uzaklık, arazi kullanımı örtüsü, yükseklik, eğim, bakı ve genel eğrilik faktörleri değerlendirmeye alınmıştır. Heyelan envanterinin %70’i eğitim verisi, %30’u doğrulama verisi olarak kullanılmıştır. Heyelan duyarlılık sonuçlarından kategorik heyelan duyarlılık haritasının oluşturulmasında Eşit Aralıklı, Doğal Aralıklı, Geometrik Aralıklı ve Kuantil Sınıflandırma teknikleri kullanılmış ve heyelan duyarlılığı Çok Yüksek, Yüksek, Orta, Düşük ve Çok Düşük olmak üzere beş sınıfa kategorilendirilmiştir. ROC (İşlem Karakteristik Eğrisi) analizi ve SCAI (Doğrulama Pikseli Alan İndeksi) indeksi ile heyelan duyarlılık haritalarının doğruluk değerlendirmesi gerçekleştirilmiş ve Doğal Aralıklı Sınıflandırma yönteminin daha iyi sonuç verdiği tespit edilmiştir. Doğal Aralıklı Sınıflandırma yöntemi sonucuna göre ilin %17,2’si Çok Yüksek, %27,5’i Yüksek, %27,7’si Orta, %20,0’ı Düşük ve %7,6’sı Çok Düşük heyelan duyarlılığı göstermektedir. Heyelan duyarlılık haritasının arazi kullanımı/örtüsü katmanı ile çakıştırılması sonucunda ilde yerleşim ve endüstriyel alanların 0,2 km2’sinin Çok Yüksek, 3,6 km2’sinin Yüksek heyelan duyarlılığında olduğu belirlenmiştir. Sonuç olarak, Frekans Oranı yöntemiyle elde edilen analiz sonuçlarından farklı sınıflandırma teknikleri ile optimum kategorik heyelan haritasının elde edilebileceği ve gelecekteki muhtemel heyelanlar için tehlike altında bulunan alanların öngörüsünde kullanılarak afet yönetimi ve planlama çalışmalarına entegre edilebileceği görülmüştür.

Keywords

Heyelan Duyarlılığı, Frekans Oranı, Coğrafi Bilgi Sistemleri, Doğal aralıklı sınıflandırma, ROC analizi, SCAI indeksi

Analysis of Landslide Susceptibility of Van Province Using Frequency Ratio Method

Abstract

In this study, the landslide susceptibility of Van province was determined using the Frequency Ratio method in the Geographical Information Systems environment. In the landslide susceptibility analysis; lithology, distance to fault lines, land use/cover, elevation, slope, aspect, and general curvature were taken into consideration. 70% of the landslide inventory was used as training data and 30% as test data. To obtain the categorical landslide susceptibility map from the landslide susceptibility analysis results, classification techniques of Equal Interval, Natural Breaks, Geometric Interval, and Quantile were used and landslide susceptibility was categorized into five classes as Very High, High, Medium, Low, and Very Low. The accuracy of the landslide susceptibility maps was evaluated by ROC (Receiver Operating Characteristic) analysis and SCAI (Seed Cell Area Index) index, and it was determined that the Natural Breaks Classification method gave better results. According to the result of the Natural Breaks Classification method, 17.2% of the province had Very High, 27.5% High, 27.7% Medium, 20.0% Low, and 7.6% Very Low landslide susceptibility. As a result of overlapping the landslide susceptibility map with the land use/cover layer, it was determined that 0.2 km2 of the residential and industrial areas in the province had Very High and 3.6 km2 had High landslide susceptibility. As a result, it has been seen that the optimum categorical landslide map can be selected by different classification techniques from the analysis results obtained by the Frequency Ratio method, and it can be integrated into disaster management and planning studies by using it in the prediction of endangered areas for possible future landslides. 

Keywords

Landslide Susceptibility, Frequency Ratio, Geographical Information Systems, SCAI, Natural breaks classification, ROC

References

• 1. Acharya, T.D., Yang, I.T. ve Lee, D.H. (2017) GIS-based landslide susceptibility mapping of Bhotang, Nepal using frequency ratio and statistical index methods, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 35(5), 357-364. doi:10.7848/ksgpc.2017.35.5.357
• 2. Anis, Z., Wissem, G., Vali, V., Smida, H. ve Essghaier, G.M. (2019) GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia, Open Geosciences, 11(1), 708-726. doi:10.1515/geo-2019-0056
• 3. Ayalew, L., Yamagishi, H. ve Ugawa, N. (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan, Landslides, 1(1), 73-81. doi:10.1007/s10346-003-0006-9
• 4. Bai, S.B., Wang, J., Lu, G.N., Zhou, P.G., Hou, S.S. ve 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:10.1016/j.geomorph.2009.09.025
• 5. Bui, D.T., Lofman, O., Revhaug, I. ve 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:10.1007/s11069-011-9844-2
• 6. Chien, T.W., Wang, H.Y., Hsu, C.F. ve Kuo, S.C. (2019) Choropleth map legend design for visualizing the most influential areas in article citation disparities: A bibliometric study, Medicine, 98(41). doi:10.1097/MD.0000000000017527
• 7. Chung, C.J.F. ve Fabbri, A.G. (2003) Validation of spatial prediction models for landslide hazard mapping, Natural Hazards, 30(3), 451-472. doi:10.1023/B:NHAZ.0000007172.62651.2b
• 8. Clark Labs (2012) Idrisi Selva 17.0, Clark University, Worcester, USA.
• 9. Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J.D. ve Chacón, J. (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain), Natural Hazards and Earth System Sciences, 12, 327-340. doi:10.5194/nhess-12-327-2012
• 10. Cruden, D.M. (1991) A simple definition of a landslide, Bulletin of the International Association of Engineering Geology, 43, 27-29. doi:10.1007/BF02590167
• 11. Çölleşme ve Erozyonla Mücadele Genel Müdürlüğü (2016). Heyelanlar, 36 s.
• 12. Dağ, S. (2007) Çayeli (Rize) ve çevresinin istatistiksel yöntemlerle heyelan duyarlılık analizi. Doktora Tezi, Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü, Trabzon.
• 13. Dağ, S., Akgün, A., Kaya, A., Alemdağ, S. ve Bostancı, H.T. (2020) Medium scale earthflow susceptibility modelling by remote sensing and Geographical Information Systems based multivariate statistics approach: An example from Northeastern Turkey, Environmental Earth Sciences, 79(19), 1-21. doi:10.1007/s12665-020-09217-7
• 14. Dağ, S., Bulut, F., Alemdağ, S. ve Kaya, A. (2011) Heyelan duyarlılık haritalarının üretilmesinde kullanılan yöntem ve parametrelere ilişkin genel bir değerlendirme, Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 1(2), 151-176.
• 15. Dai, F.C. ve Lee, C.F. (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong, Geomorphology, 42(3-4), 213-238. doi:10.1016/S0169-555X(01)00087-3
• 16. Deng, X., Li, L. ve Tan, Y. (2017) Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity, ISPRS International Journal of Geo-Information, 6(4), 103. doi:10.3390/ijgi6040103
• 17. Dereli, M. A. (2019) Giresun iline ait Coğrafi Bilgi Sistemleri destekli heyelan duyarlılık haritalarının üretilmesi, 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun. doi:10.36287/setsci.4.6.132
• 18. Dou, J., Tien Bui, D., Yunus, A.P., Jia, K., Song, X., Revhaug, I., Xia, H. ve Zhu, Z. (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan, PLOS One, 10(7), e0133262. doi:10.1371/journal.pone.0133262
• 19. Dragićević, S., Lai, T. ve Balram, S. (2015) GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments, Habitat international, 45, 114-125. doi:10.1016/j.habitatint.2014.06.031
• 20. Eker, R. ve Aydın, A. (2014) Ormanların heyelan oluşumu üzerindeki etkileri, Türkiye Ormancılık Dergisi, 15(1), 84-93.
• 21. El Jazouli, A., Barakat, A. ve Khellouk, R. (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco), Geoenvironmental Disaster, 6, 3. doi:10.1186/s40677-019-0119-7
• 22. Erener, A. ve Duzgun, H.S. (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway), Landslides, 7, 55-68. doi:10.1007/s10346-009-0188-x
• 23. Erener, A. ve Lacasse, S. (2007) Heyelan duyarlılık haritalamasında CBS kullanımı, TMMOB Harita ve Kadastro Mühendisleri Odası Ulusal Coğrafi Bilgi Sistemleri Kongresi, 30 Ekim-02 Kasım 2007, KTÜ, Trabzon.
• 24. ESRI (2008) ArcGIS 9.3, Redlands, CA, USA.
• 25. Fayez, L., Pham, B.T., Solanki, H.A., Pazhman, D., Dholakia, M.B., Khalid, M. ve Prakash, I. (2018) Application of frequency ratio model for the development of landslide susceptibility mapping at Part of Uttarakhand State, India, International Journal of Applied Engineering Research, 13, 6846-6854.
• 26. Flores-Garnica, J.G. ve Flores-Rodríguez, A.G. (2020) Comparative analysis of the number and intervals of forest fire risk classes, Revista Mexicana de Ciencias Forestales, 11(62), 4-30. doi:10.29298/rmcf.v11i62.775
• 27. Gökçeoğlu, C. ve Ercanoğlu, M. (2001) Heyelan duyarlılık haritalarının hazırlanmasında kullanılan parametrelere ilişkin belirsizlikler, Yerbilimleri Dergisi, 22(23), 189-206.
• 28. Guzzetti, F., Carrara, A., Cardinali, M. ve Reichenbach, P. (1999) Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31(1-4), 181-216. doi:10.1016/S0169-555X(99)00078-1
• 29. Harita Genel Müdürlüğü (2015) Türkiye mülki idare bölümleri haritası. Erişim adresi: https://www.harita.gov.tr/urun/turkiye-mulk-idare-bolumleri-haritasi/189
• 30. Hervás, J. ve Bobrowsky, P. (2009) Mapping: Inventories, Susceptibility, Hazard and Risk. İçinde: Sassa K., Canuti P. (editörler) Landslides – Disaster Risk Reduction, Springer, Heidelberg. doi:10.1007/978-3-540-69970-5_19
• 31. Hong, H., Naghibi, S.A., Pourghasemi, H.R. ve Pradhan, B. (2016) GIS-based landslide spatial modeling in Ganzhou City, China, Arabian Journal of Geosciences, 9, 112. doi:10.1007/s12517-015-2094-y
• 32. Intarawichian, N. ve Dasananda, S. (2011) Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand, Environmental Earth Sciences, 64(8), 2271-2285. doi:10.1007/s12665-011-1055-3
• 33. Kadıoğlu, M. (2011) Afet Yönetimi: Beklenilmeyeni Beklemek, En Kötüsünü Yönetmek, T.C. Marmara Belediyeler Birliği Yayını, Yayın No: 65.
• 34. Karaca, S., Sarğın, B. ve Türkmen, F. (2019) Bazı arazi ve toprak niteliklerinin coğrafi bilgi sistem analizleriyle incelenmesi: Van ili arazi ve toprak özellikleri, Türkiye Tarımsal Araştırmalar Dergisi, 6(2), 199-205. doi:10.19159/tutad.542543
• 35. Kavzoğlu, T., Şahin, E.K. ve Çölkesen, İ. (2012) Heyelan duyarlılığının incelenmesinde regresyon ağaçlarının kullanımı: Trabzon örneği, Harita Dergisi, 147, 21-33.
• 36. Khan, H., Shafique, M., Khan, M.A., Bacha, M.A., Shah, S.U. ve Calligaris, C. (2019) Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan, The Egyptian Journal of Remote Sensing and Space Science, 22(1), 11-24. doi:10.1016/j.ejrs.2018.03.004
• 37. Lee, S. ve Pradhan, B. (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models, Landslides, 4(1), 33-41. doi:10.1007/s10346-006-0047-y
• 38. Lee, S. ve Talib, J.A. (2005) Probabilistic landslide susceptibility and factor effect analysis, Environmental Geology, 47(7), 982-990. doi:10.1007/s00254-005-1228-z
• 39. Li, B., Wang, N. ve Chen, J. (2021) GIS-based landslide susceptibility mapping using information, frequency ratio, and artificial neural network methods in Qinghai Province, Northwestern China, Advances in Civil Engineering. doi:10.1155/2021/4758062
• 40. Maden Tetkik ve Arama Genel Müdürlüğü (2007) Van İlinin Yerbilim Verileri, Ankara, 158 s.
• 41. Mandal, S. ve Mondal, S. (2019) Statistical Approaches For Landslide Susceptibility Assessment and Prediction, Springer International Publishing, Cham, 193 s.
• 42. Mandrekar, J.N. (2010). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5(9), 1315-1316. doi:10.1097/JTO.0b013e3181ec173d
• 43. Mas, J.F., Soares Filho, B., Pontius, R.G., Farfán Gutiérrez, M. ve Rodrigues, H. (2013) A suite of tools for ROC analysis of spatial models, ISPRS International Journal of Geo-Information, 2(3), 869-887. doi:10.3390/ijgi2030869
• 44. Meten, M., Bhandary, N.P. ve Yatabe, R. (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia, Journal of Mountain Science, 12(6), 1355-1372. doi:10.1007/s11629-015-3464-3
• 45. Mirdda, H.A., Bera, S., Siddiqui, M.A. ve Singh, B. (2020) Analysis of bi-variate statistical and multi-criteria decision-making models in landslide susceptibility mapping in lower Mandakini Valley, India, GeoJournal, 85(3), 681-701. doi:10.1007/s10708-019-09991-3
• 46. Moreira, L.L., de Brito, M.M. ve Kobiyama, M. (2021) Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes, Water, 13, 98. doi:10.3390/w13010098
• 47. Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B.T., Lee, S. ve Melesse, A.M. (2019) Landslide susceptibility mapping using different GIS-based bivariate models, Water, 11(7), 1402. doi:10.3390/w11071402
• 48. Öztürk, K. (2002) Heyelanlar ve Türkiye’ye etkileri, G.Ü. Gazi Eğitim Fakültesi Dergisi, 22(2), 35-50.
• 49. Park, S., Choi, C., Kim, B. ve Kim, J. (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea, Environmental Earth Sciences, 68(5), 1443-1464. doi:10.1007/s12665-012-1842-5
• 50. Pawluszek-Filipiak, K., Oreńczak, N. ve Pasternak, M. (2020) Investigating the effect of cross-modeling in landslide susceptibility mapping, Applied Sciences, 10(18), 6335. doi:10.3390/app10186335
• 51. Pham, B.T., Tien Bui, D., Indra, P. ve Dholakia, M. (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method, International Journal of Engineering Research & Technolgy, 4(11), 338-344. doi:10.17577/IJERTV4IS110285
• 52. Pourghasemi, H.R., Moradi, H.R. ve Aghda, S.F. (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances, Natural hazards, 69(1), 749-779. doi:10.1007/s11069-013-0728-5
• 53. Pradhan, B. ve Lee, S. (2010) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environmental Modelling&Software, 25(6), 747-759. doi:10.1016/j.envsoft.2009.10.016
• 54. Rahardianto, T., Saputra, A. ve Gomez, C. (2017) Assessment of landslide distribution map reliability in Niigata prefecture-Japan using frequency ratio approach, AIP Conference Proceedings, 1857(1), 060003, AIP Publishing LLC.
• 55. Rasyid, A.R., Bhandary, N.P. ve Yatabe, R. (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia, Geoenvironmental Disasters, 3, 19. doi:10.1186/s40677-016-0053-x
• 56. Rawat J.S. ve Kumar M. (2015) Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India, Egyptian Journal of Remote Sensing and Space Science, 18(1), 77-84. doi:10.1016/j.ejrs.2015.02.002
• 57. Ross, S.M. (2014) Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 670 s.
• 58. Sharma, M. ve Gupta, A. (2017) The Practice of Business Statistics: Using Statistics for Decision Making. Khanna Book Publishing, 809 s.
• 59. Silalahi, F.E.S., Arifianti, Y. ve Hidayat, F. (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia, Geoscience Letters, 6(1), 1-17. doi:10.1186/s40562-019-0140-4
• 60. Thapa, D. ve Bhandari, B. P. (2019) GIS-Based frequency ratio method for identification of potential landslide susceptible area in the Siwalik zone of Chatara-Barahakshetra section, Nepal, Open Journal of Geology, 9(12), 873. doi:10.4236/ojg.2019.912096
• 61. Van Valiliği Çevre ve Şehircilik İl Müdürlüğü (2020) Van İli 2019 Yılı Çevre Durum Raporu, 146 s.
• 62. Wang, Q. ve Li, W. (2017) A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping, Physical Geography, 38(4), 318-337. doi:10.1080/02723646.2017.1294522
• 63. Wang, Q., Li, W., Xing, M., Wu, Y., Pei, Y., Yang, D. ve Bai, H. (2016) Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models, Geosciences Journal, 20(5), 705-718. doi:10.1007/s12303-016-0003-3
• 64. Wubalem, A. (2021) Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia, Geoenvironmental Disasters, 8(1), 1-21. doi:10.21203/rs.3.rs-15731/v2
• 65. Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J. ve Travé, A. (2021) Large-scale landslide susceptibility mapping using an integrated machine learning model: A case study in the Lvliang mountains of China, Frontiers in Earth Science, 622. doi:10.3389/feart.2021.722491
• 66. Yoo, Y., Baek, T., Kim, J. ve Park, S. (2016) A Comparative Study of the Frequency Ratio and Evidential Belief Function Models for Landslide Susceptibility Mapping, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 34(6), 597-607. doi:10.7848/ksgpc.2016.34.6.597
• 67. Youssef, A.M., Al-Kathery, M. ve Pradhan, B. (2015) Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models, Geosciences Journal, 19(1), 113-134. doi:10.1007/s12303-014-0032-8
• 68. https://www.harita.gov.tr/urun/il-ve-ilce-yuzolcumleri/176, Erişim Tarihi: 15.02.2021, Konu: İl ve İlçe Yüzölçümleri.
• 69. https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2020-37210, Erişim Tarihi: 17.05.2021, Konu: Nüfus.
• 70. https://mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=H&m=VAN, Erişim Tarihi: 08.07.2021, Konu: Resmi istatistikler.