Bulanık Uyarlanabilir Rezonans Teorisi (FuzzyART) Yöntemi Kullanılarak Heyelan Duyarlılık Analizi: Tonya (Trabzon) Örneği

Yıl 2018, Cilt: 8 Sayı: 1, 135 - 146, 31.01.2018

Aykut Akgün

https://doi.org/10.17714/gumusfenbil.346532

Cited By: 4

Öz

Bu çalışmada, şimdiye
kadar heyelan duyarlılık analizi ile ilgili literatürde kullanılmamış olan
bulanık uyarlanabilir rezonans teorisi (FuzzyART-BURT) olarak isimlendirilen ve
esasında bir küme sınıflandırıcı olan yöntemin, heyelan duyarlılık haritası
üretiminde kullanılması amaçlanmıştır. Bu amaç için, çalışma alanı olarak
çoğunlukla sığ kayma yüzeyli heyelanların sıklıkla izlendiği ve bu nedenle
geçmişten günümüze heyelan nedenli afet sürecine maruz kalan Trabzon iline
bağlı Tonya ilçesi seçilmiştir. İlk olarak çalışma alanına ait, heyelan
duyarlılık analizinde ihtiyaç duyulan hazırlayıcı nedenlerden litoloji,
yükseklik, yamaç eğimi, yamaç yönelimi, akarsu güç indeksi (AGİ) ve topoğrafik
nemlilik indeksi (TNİ) verileri elde edilmiştir. Alanda daha önceden meydana
gelmiş ve geçmiş olay envanterini yansıtan heyelan envanter verisi çalışmada
bağımlı değişken olarak kullanılmıştır. Elde edilen bu verilerden ilk olarak
her bir hazırlayıcı parametre için frekans oranı değerleri hesaplanmış,
hesaplanan bu frekans oranı değerlerinden itibaren her bir parametrede ayırt
edilen alt sınıflar için duyarlılık sınıfları belirlenmiştir. Belirlenen bu
duyarlılık sınıfları, uygulanan BURT yönteminde eğitim parametreleri olarak
kullanılmıştır. BURT ile eğitilen her bir hazırlayıcı parametre verisinden o
parametreye ait duyarlılık haritası üretildikten sonra, elde edilen tüm
duyarlılık haritalarının bir araya toplanması sonucunda inceleme alanına ait
nihai heyelan duyarlılık haritası elde edilmiştir. Üretilen sonuç heyelan
duyarlılık haritasının doğruluk analizinin yapılabilmesi amacıyla, eğri
altındaki alan (ROC-EAA) yöntemi kullanılmıştır. Yapılan doğrulama analizi
sonucunda EAA değeri 0.72 olarak belirlenmiş olup bu değer üretilen heyelan
duyarlılık haritasının oldukça başarılı bir kestirime sahip olduğunu
göstermektedir.

Anahtar Kelimeler

Doğal afet, Duyarlılık, Heyelan, Trabzon

Landslide Susceptibility Analysis by Fuzzy Adaptive Resonance Theory (FuzzyART) Method: Tonya (Trabzon) Example

Öz

In this study, use of the method called as fuzzy adaptive resonance
theory (FuzzyART) which is esentially a cluster classifier and has never been
used in landslide susceptibility literature was aimed in producing of landslide
susceptibility map. For this purpose, Tonya district belonging to Trabzon city
where shallow seated landslides occasionally happens and exposed to several
landslide-based hazards was chosen as study area.  Initially, lithology, altitude, slope
gradient, slope aspect, stream power index (SPI) and topographical wetness index
(TWI) data belonging to the study area were chosen to be conditioning
parameters needed for landslide susceptibility analysis. The landslide
inventory data showing past case inventory was used to be independent
parameter. From the conditioning parameters obtained, likelihood ratio (LR) values
for each of these parameters were calculated, and based on the calculated LR
values, susceptibility classes were determined for each of the sub-classes of
the each parameters. These susceptibility classes were used as training
parameter in the Fuzzy ART model applied. After obtaining the susceptibility
maps of each parameter from each preliminary parameter database trained by
FuzzyART, the final landslide susceptibility map of the study area was obtained
as a result of the collection of all the susceptibility maps obtained. To
perform the validation of the produced susceptibility map, area under curvature
(AUC) method was used. At the end of the validation analysis, the AUC value was
obtained to be 0.72, and this value shows that the susceptibility map produced
has a good prediction capability.

Anahtar Kelimeler

Natural hazard, Susceptibility, Landslide, Trabzon

Kaynakça

• Akgün, A. ve Bulut, F., (2007). GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region, Environmental Geology, 51, 1377-1387.
• Akgün, A. ve Erkan, O., 2016. Landslide susceptibility mapping by geographical information systems-based multivariate statistical and deterministic models: In an artificial reservoir area at Northern Turkey, Arabian Journal of Geosciences, 9,165,1-15.
• Akgün, A. ve Türk, N., 2010. İki ve Çok Değişkenli İstatistik ve Sezgisel Tabanlı Heyelan Duyarlılık Modellerinin Karşılaştırılması: Ayvalık (Balıkesir, Kuzeybatı Türkiye) Örneği, Jeoloji Mühendisliği Dergisi, 34(2), 85-112.
• 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.
• Akgün, A., Sezer E.A., Nefeslioglu, H.A., Gökçeoğlu, C. ve Pradhan, B., 2012. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Computers and Geosciences, 38(1), 23–34.
• Althuwaynee, O.F., Pradhan B, ve Lee, S., 2012. Application of an evidential belief function model in landslide susceptibility mapping, Computers and Geosciences, 44, 120–135.
• Carpenter, G. A., 1989. Neural Network Models for Pattern Recognition and Associative Memory, Neural Networks, 2, 243-257.
• Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., ve Rosen, D. B., 1992. Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps, IEEE Transactions on Neural Networks, 3(5), 698-713.
• Carpenter, G. A., Grossberg, S., ve Reynolds, J. H., 1991. ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network, Neural Networks, 4, 565-588.
• Carrara, A., Cardinali, M., Guzzetti, F. ve Reichenbach, P. 1995, GIS technology in mapping landslide hazard. Carrara, A. and Guzzetti, F (eds.), Geographical Information Systems in assessing natural hazards, Dordrecht: Kluwer. pp.135-175.
• Castellanos Abella, E.A. ve Van Westen, C.J., 2007. Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation, Landslides, 4, 311–325.
• Chacon, J., Irigaray, C., Fernandez, T. ve El Hamdouni, R., 2006. Engineering geology maps: landslides and geographical information systems, Bulletin of Engineering Geology and Environment, 65, 341–411.
• Çan, T., Nefeslioğlu, H.A., Gökçeoğlu, C., Sönmez, H. ve Duman, T.Y., 2005. Susceptibility assessment of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses, Geomorphology, 72, 250– 271.
• Çevik, E. ve Topal, T., 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey), Environmental Geology, 44, 949–962.
• Çölkesen, I., Kutluğ Sahin, E. ve Kavzoglu, T., 2016. Susceptibility Mapping of Shallow Landslides Using Kernel-Based Gaussian Process, Support Vector Machines and Logistic Regression, Journal of African Earth Sciences,118, 53-64.
• Dağ, S., Bulut, F., Alemdağ, S. ve Kaya, A., 2011. Heyelan Duyarlılık Haritalarının Üretilmesinde Kullanılan Yöntem ve Parametrelere İlişkin Genel Bir Değerlendirme, Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi,1, 2, 151-176.
• Dağdelenler G., Nefeslioğlu H.A. ve Gökçeoğlu 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, 575-590.
• Demir, G., Aytekin,M., Akgün, A., İkizler, S.B. ve 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, Natural Hazards, 65,1481–1506.
• Duman, T. Y., Nefeslioğlu, H.A.., Çan, T., Olgun, Ş., Durmaz, S., Hamzaçebi, S. ve Çörekçioğlu, Ş., 2007. 1:500.000 Ölçekli Türkiye Heyelan envanter Haritası, Trabzon Paftası, MTA Özel Yayın Serisi-9.
• Eker, A.M., Dikmen, M., Cambazoğlu, S., Düzgün, H.S.B. ve Akgün, H., 2015. Evaluation and Comparison of Landslide Susceptibility Mapping Methods: A Case Study for the Ulus District, Bartın, Northern Turkey, International Journal of Geographical Information Science, 29, 132-158.
• Ercanoğlu, M. ve Gökçeoğlu, C., 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach, Environmental Geology, 41, 720–730.
• Ercanoğlu, M., Dağdelenler, G., Özsayın, E., Alkevli, T., Sönmez, H., Özyurt, N. N., Kahraman, B., Uçar, İ. ve Çetinkaya, S., 2016. Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region, Journal of Mountain Sciences, 13, 1923-1940.
• Erener, A., Mutlu, A. ve Düzgün, H,S., 2016. A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM), Engineering Geology, 203, 45-55.
• Fawcett,T., 2006. An introduction to ROC analysis, Pattern Recognition Letters, 27, 861- 874.
• Gökçeoğlu, C. ve 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, 147-161.
• Gökçeoğlu, C. ve Ercanoglu, M., 2001. Heyelan duyarlılık haritalarının hazırlanmasında kullanılan parametrelere ilişkin belirsizlikler, Yerbilimleri, 23, 189-206.
• Guantanamo, Cuba, Computers and Geosciences, 37, 410-425.
• Gurocak, Z., Alemdag, S., Bostanci, H.T., ve Gokceoglu, C., 2017. Discontinuity controlled slope failure zoning for a granitoidcomplex: A fuzzy approach.Rock Mechanics and Engineering, Volume 5: Surfaceand Underground Projects, CRC Press Taylor & Francis Group, eBook ISBN: 978-1-317-48188-1, Pages 1–25.
• Guzetti, F., Carrarra, A., Cardinali, M. ve Reichenbach, P., 1999. Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy, Geomorphology, 31, 181-216.
• Ildır, B. 1995. Türkiye’de heyelanların dagılımı ve afetler yasası ile ilgili uygulamalar. Onalp A (ed) 2. Ulusal Heyelan Sempozyumu, Sakarya Üniversitesi, Türkiye, pp 1–9.
• Kavzoğlu, T., Şahin, E.K., ve Çölkesen, I.,2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression, Landslides,11(3), 425-439.
• Kıncal, C., Akgün, A. ve Koca, M.Y., 2009. Landslide susceptibility assessment in the Izmir (West Anatolia,Turkey) city center and its near vicinity by the logistic regression method, Environmental Earth Sciences, 59, 745-756.
• Lee, S., Choi, J. ve Min, K., 2004. Landslide hazard mapping using GIS and remote sensing data at Boun, Korea, International Journal of Remote Sensing, 25, 2037-2052.
• M.T.A.,1998. 1:100.000 ölçekli Jeoloji Haritaları, Trabzon F42 Paftası, M.T.A. Yayınları, Ankara.
• Mannan, B. ve Roy, J., 1998. Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images, International Journal of Remote Sensing, 19, 767-774.
• Melchiorre, C., Castellanos, E.A. Van Westen, C.J. ve Matteucci, M., 2011. Evaluation of prediction capability, robustness and sensitivity in non linear landslide susceptibility models,
• Moore, I.D., Grayson, R.B. ve Ladson, A.R., 1991. Digital terrain modeling: a review of hydrological, geomorphological and biological applications, Hydrological Processes, 5, 3-30.
• Nandi, A. ve Shakoor, A., 2009. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses, Engineering Geology, 110, 11–20.
• Nefeslioğlu H.A., Sezer E.A., Gökçeoğlu, C. ve Ayaş, Z., 2013. A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments, Computers and Geosciences, 59, 1–8.
• Nefeslioğlu, H.A., Duman, T.Y. ve Durmaz, S., 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey), Geomorphology, 94, 401–418.
• Nefeslioğlu, H.A., Sezer,E., Gökçeoğlu, C., Bozkır, A.S.ve Duman, T.Y., 2010. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of İstanbul, Turkey, Mathematical Problems in Engineering, 2010, 1-15.
• Ösna, T., Sezer E.A. ve Akgün, A., 2014. GEOFIS: an integrated tool for the assessment of landslide susceptibility, Computers and Geosciences, 66, 20–30.
• Pradhan, B., 2011. Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis, Environmental and Ecological Statistics, 18, 471–493.
• Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers and Geoscience, 51, 350-365.
• Pradhan, B., Sezer, E.A., Gökçeoğlu, C. ve Buchroithner, M.F., 2010. Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia), IEEE Transactions on Geosciences Remote Sensing, 48,4164–4177
• Romer, C. ve Ferentinou, M., 2016. Shallow landslide susceptibility assessment in a semiarid environment—A Quaternary catchment of KwaZulu-Natal, South Africa, Engineering Geology, 201, 29-44.
• Roodposhti, M.S., Rahimi, S. ve Beglou, M.J., 2013. PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping, Natural Hazards, 73, 77–95.
• Sezer, E.A., Pradhan, B. ve Gökçeoğlu, C., 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia, Expert Systems and Applications, 38, 8208–8219.
• Süzen, M.L. ve Doyuran, V., 2004. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey, Engineering Geology, 71, 303-321.
• Thiebes, B., 2011, Landslide analysis and early warning—local and regional case study in the Swabian Alb. Doktora Tezi, University of Vienna, Vienna, 295s.
• URL-1,www.meteor.gov.tr. 25 Eylül 2017
• USGS, 1993., Data user guide 5 for DEM’s. ftp://mapping.usgs.gov/pub/ti/DEM/demguide.
• Varnes, D.J., 1978, Slope movement types and processes. Landslides Analysis and Control. Special Report. Schuster, R.L., Krizek, R.J. (eds.), National Academy of Sciences, New York. pp. 12- 33.
• Yeşilnacar, E. ve Topal, T., 2005. Landslide susceptibility mapping: A comparision of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Engineering Geology, 79, 251-266.
• Yılmaz, I., 2009. A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by Artificial Neural Networks, Bulletin of Engineering Geology and the Environment, 68 (3), 297-306.
• Yılmaz, I., 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional Probability, Logistic Regression, Artificial Neural Networks, and Support Vector Machine, Environmental Earth Sciences, 61, 821-836.