İki ve Çok Değişkenli İstatistik ve Sezgisel Tabanlı Heyelan Duyarlılık Modellerinin Karşılaştırılması: Ayvalık (Balıkesir, Kuzeybatı Türkiye) Örneği

Makalenin İngilizce İsmi: 
Comparison of Bivariate and Multivariate Statistical and Heuristic-Based Landslide Susceptibility Models: an Example From Ayvalık (Balıkesir, Northwestern Turkey)
Makale İçerik Bilgileri
Makale Dili: 
Türkçe
Anahtar Kelimeler: 
Analitik hiyerarşi
Ayvalık
Benzerlik oranı
Heyelan
Mantıksal regresyon
Türkçe Özet: 

Heyelanlar, ülkemizde ve dünyada depremlerden sonra en fazla sıklıkla meydana gelen ve en çok
zarar verici potansiyele sahip doğal afetlerden birisidir. Mühendislik açısından, heyelan zararlarının en aza
indirilmesi amacıyla, heyelan olayının önceden tahmin edilmesi büyük önem taşımakta olup, olasılığa
dayalı yöntemlerle heyelana duyarlı alanların belirlenmesi, özellikle son yirmi yılda, gerek dünyada
gerekse ülkemizde oldukça yaygınlaşmıştır. Bu çalışma kapsamında, heyelan duyarlılık haritalarının
hazırlanmasında en fazla kullanılan yöntemlerden iki ve çok değişkenli istatistik yöntemler ile sezgisel
yöntemin karşılaştırması yapılmıştır. Amaca yönelik olarak, Ayvalık ilçesi (Balıkesir) ve yakın çevresi
inceleme alanı olarak seçilmiş ve toplam 45 heyelan haritalanmıştır. Morfolojik, jeolojik ve arazi kullanımı
verileri, Coğrafi Bilgi Sistemleri (CBS) kapsamında mevcut topoğrafik ve ilgili tematik haritalar
kullanılarak üretilmiştir. Çalışma alanında, heyelana neden olan parametreler olarak; yamaç eğimi ve
yönelimi, litoloji, kayaların ayrışma durumu, akarsu gücü indeksi (AGİ), topoğrafik nemlilik indeksi
(TNİ), drenaj ağından uzaklık, yapısal unsurların yoğunluğu, arazi ve bitki örtüsü yoğunluğu dikkate
alınmıştır. Bu heyelan parametreleri, bulanık üyelik fonksiyonları yardımıyla ortak bir ölçekte
standartlaştırılmıştır. Daha sonra, her bir parametrenin heyelan oluşumuna katkısı; benzerlik oranı,
mantıksal regresyon ve analitik hiyerarşi yöntemleri kullanılarak incelenmiş ve bu parametrelerin ağırlık
değerleri hesaplanmıştır. Her bir yöntemle belirlenen ağırlık değerleri dikkate alınarak heyelan duyarlılık
haritaları üretilmiş, üretilen haritaların performansları, mevcut heyelan lokasyonları ile karşılaştırılarak
Eğri Altındaki Alan (EAA) yaklaşımıyla sınanmıştır. Buna göre, EAA değerleri sırasıyla benzerlik oranı
yöntemi için 0.76, mantıksal regresyon için 0.77 ve analitik hiyerarşi yöntemi için 0.89 olarak
belirlenmiştir. Bu sonuçlara göre inceleme alanı için en başarılı heyelan duyarlılık değerlendirmesinin,
analitik hiyerarşi yöntemi ile olduğu görülmüştür.

Key Words: 
Analytical hierarchy
Ayvalık
likelihood ratio
Landslide
Logistic Regression
İngilizce Özet: 

Landslides are one of the most destructive natural hazards which frequently occur after earthquakes
in our country and in the world. From engineering point of view, prediction of landsliding before its
occurence has a great importance to mitigate the landslide related damages, and determination of
landslide prone areas by the methods, based on probability, has spread out both in our country and in the
world in the last two decades. In this study, a comparison of the most common landslide susceptibility
mapping methods, namely bivariate, multivariate statistical and heuristic methods, were carried out. For
this purpose, Ayvalık (Balıkesir) and its near vicinity were selected as study area, and in total 45
landslides were mapped. Morphologic, geologic and land-use data were produced in Geographical
Information Systems (GIS) by using available topographical and relevant thematic maps. In the area,
slope gradient and aspect, lithology, weathering conditions of the rocks, stream power index (SPI),
topographical wetness index (TWI), distance from drainage, density of structural features, land-cover and
vegetation cover density were considered as the parameters causing the landslides. All of the parameters
were standardized in a common scale by using fuzzy membership functions. Then, the contribution of each
of these parameters for the landslide occurrence were investigated by likelihood ratio, logistic regression
and analytical hierarchy methods, and the weight values of the parameters were calculated. Considering
the weight values determined by each method, landslide susceptibility maps were produced, and the
performances of the produced maps were tested by comparing landslide locations using Area Under
Curvature (AUC) approach. Based on this, the AUC values were determined to be 0.76, 0.77 and 0.89 for
likelihood ratio, logistic regression and analytical hierarchy models, respectively. Accorrding to these
results, analytical hierarcy model was considered to be the best landslide susceptibility method for the
study area.

Yazar Bilgileri
1. Yazar
Yazar Adı: 
Aykut AKGÜN1
Yazar Üniversitesi: 
Karadeniz Teknik Üniversitesi
Yazar Anabilim Dalı: 
Jeoloji Mühendisliği
2. Yazar
Yazar Adı: 
Necdet TÜRK
Yazar Üniversitesi: 
Dokuz Eylül Üniversitesi
Yazar Anabilim Dalı: 
Jeoloji Mühendisliği
Makale Künye Bilgisi
Makalenin Yayımlandığı Dergi: 
Jeoloji Mühendisliği Dergisi
Makale Yayın Yılı: 
2010
Cilt/Sayı: 
34
Sayı: 
2
Sayfa Aralığı: 
85-112
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2008. Doline probability map using logistic
regression and GIS technology in the central
Ebro Basin (Spain). Environmental Geology, 54,
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Lee, S., 2005. Application of logistic regression
model and its validation for landslide
susceptibility mapping using GIS and remote
sensing data. International Journal of Remote
Sensing, 26, 1477-1491.
Lee, S., Min, K., 2001. Statistical analysis of
landslide susceptibility at Yongin, Korea.
Environmental Geology, 40, 1095-1113.
Lee, S., Dan, N.T., 2005. Probabilistic landslide
susceptibility mapping in the Lai Chau province
of Vietnam: focus on the relationship between
tectonic fractures and landslides. Environmental
Geology, 48, 778– 787.
Lee S, Sambath, T., 2006. Landslide susceptibility
mapping in the Damrei Romel area, Cambodia
using frequency ratio and logistic regression
models. Environmental Geology, 50, 847–855.
Lee, S., Pradhan, B., 2007. Landslide hazard mapping
at Selangor, Malaysia, using frequency ratio and
logistic regression models. Landslides, 4, 33–41.
Lee, S., Choi, J., Min, K., 2004. Landslide hazard
mapping using GIS and remote sensing data at
Boun, Korea. International Journal of Remote
Sensing, 25, 2037-2052.
Malczewski, J., 1999. GIS and Multicriteria Decision
Analysis. John Wiley &Sons, Inc. USA, 392p.
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106. Thousand Oaks, California, 98p.
Moore, I.D., Grayson, R.B., Ladson, A.R., 1991.
Digital terrain modeling: a review of
hydrological, geomorphological and biological
applications. Hydrological Processes, 5, 3-30.
Nandi, A., Shakoor, A., 2009. A GIS-based landslide
susceptibility evaluation using bivariate and
multivariate statistical analyses, Engineering
Geology, 110, 11–20.
Nefeslioğlu, H.A., Duman, T.Y., Durmaz, S., 2008.
Landslide susceptibility mapping for a part of
tectonic Kelkit Valley (Eastern Black Sea region
of Turkey). Geomorphology, 94(3–4), 401–418.
Nefeslioğlu, H.A., Sezer,E., Gökçeoğlu, C., Bozkır,
A.S., Duman, T.Y., 2010. Assessment of
Landslide Susceptibility by Decision Trees in the
Metropolitan Area of İstanbul, Turkey.
Mathematical Problems in engineering,
doi:10.1155/2010/901095.
Ohlmacher, G.C., Davis, C. J., 2003. Using multiple
regression and GIS technology to predict
landslide hazard in northeast Kansas, USA.
Engineering Geology, 69, 331-343.
Pradhan, B., Lee, S., 2007. Utilization of optical
remote sensing data and GIS tools for regional
landslide hazard analysis by using an artificial
neural network model at Selangor, Malaysia.
Earth Science Frontiers, 14, 143–152.
Saaty, T.L., 1980. The Analytical Hierarchy Process.
McGraw Hill, New York. 350p.
Scott, D.W., 1992. Multivariate Density Estimation.
John Wiley, New York, 90p.
Soeters, R., van Westen, C.J., 1996. Slope instability
recognition analysis and zonation. In: Turner
K.T., Schuster, R.L. (eds.). Landslides:
investigation and mitigation . Transportation
Research Board National Reseacrh Council,
Special Report No: 247, Washington, DC, 129-
177.
Sturges, H.A., 1926. The choice of a class interval.
Journal of the American Statistical Association,
21, 65-66.
Süzen, M.L., 2002. Data Driven Landslide Hazard
Assessment Using Geograpical Information
Systems and Remote Sensing., Ph.D. Thesis,
Middle East Technical University, The Graduate
School of Natural and Applied Science, Ankara.
Süzen, M.L., Doyuran, V., 2004. Data driven
bivariate landslide susceptibility assessment Lamelas, M.T., Marinoni, O., Hoppe, A., Riva, J.,
2008. Doline probability map using logistic
regression and GIS technology in the central
Ebro Basin (Spain). Environmental Geology, 54,
963–977.
Lee, S., 2005. Application of logistic regression
model and its validation for landslide
susceptibility mapping using GIS and remote
sensing data. International Journal of Remote
Sensing, 26, 1477-1491.
Lee, S., Min, K., 2001. Statistical analysis of
landslide susceptibility at Yongin, Korea.
Environmental Geology, 40, 1095-1113.
Lee, S., Dan, N.T., 2005. Probabilistic landslide
susceptibility mapping in the Lai Chau province
of Vietnam: focus on the relationship between
tectonic fractures and landslides. Environmental
Geology, 48, 778– 787.
Lee S, Sambath, T., 2006. Landslide susceptibility
mapping in the Damrei Romel area, Cambodia
using frequency ratio and logistic regression
models. Environmental Geology, 50, 847–855.
Lee, S., Pradhan, B., 2007. Landslide hazard mapping
at Selangor, Malaysia, using frequency ratio and
logistic regression models. Landslides, 4, 33–41.
Lee, S., Choi, J., Min, K., 2004. Landslide hazard
mapping using GIS and remote sensing data at
Boun, Korea. International Journal of Remote
Sensing, 25, 2037-2052.
Malczewski, J., 1999. GIS and Multicriteria Decision
Analysis. John Wiley &Sons, Inc. USA, 392p.
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Analysis. Sage university paper series o
quantitative applications in social sciences, vol.
106. Thousand Oaks, California, 98p.
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Digital terrain modeling: a review of
hydrological, geomorphological and biological
applications. Hydrological Processes, 5, 3-30.
Nandi, A., Shakoor, A., 2009. A GIS-based landslide
susceptibility evaluation using bivariate and
multivariate statistical analyses, Engineering
Geology, 110, 11–20.
Nefeslioğlu, H.A., Duman, T.Y., Durmaz, S., 2008.
Landslide susceptibility mapping for a part of
tectonic Kelkit Valley (Eastern Black Sea region
of Turkey). Geomorphology, 94(3–4), 401–418.
Nefeslioğlu, H.A., Sezer,E., Gökçeoğlu, C., Bozkır,
A.S., Duman, T.Y., 2010. Assessment of
Landslide Susceptibility by Decision Trees in the
Metropolitan Area of İstanbul, Turkey.
Mathematical Problems in engineering,
doi:10.1155/2010/901095.
Ohlmacher, G.C., Davis, C. J., 2003. Using multiple
regression and GIS technology to predict
landslide hazard in northeast Kansas, USA.
Engineering Geology, 69, 331-343.
Pradhan, B., Lee, S., 2007. Utilization of optical
remote sensing data and GIS tools for regional
landslide hazard analysis by using an artificial
neural network model at Selangor, Malaysia.
Earth Science Frontiers, 14, 143–152.
Saaty, T.L., 1980. The Analytical Hierarchy Process.
McGraw Hill, New York. 350p.
Scott, D.W., 1992. Multivariate Density Estimation.
John Wiley, New York, 90p.
Soeters, R., van Westen, C.J., 1996. Slope instability
recognition analysis and zonation. In: Turner
K.T., Schuster, R.L. (eds.). Landslides:
investigation and mitigation . Transportation
Research Board National Reseacrh Council,
Special Report No: 247, Washington, DC, 129-
177.
Sturges, H.A., 1926. The choice of a class interval.
Journal of the American Statistical Association,
21, 65-66.
Süzen, M.L., 2002. Data Driven Landslide Hazard
Assessment Using Geograpical Information
Systems and Remote Sensing., Ph.D. Thesis,
Middle East Technical University, The Graduate
School of Natural and Applied Science, Ankara.
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