Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran

Yıl 2015, Cilt: 65 Sayı: 1, 30 - 40, 01.01.2015

Ebrahim Karimi Sangchini Mohammad Reza Nowjavan Abdolhossein Arami

https://doi.org/10.17099/jffiu.52751

Öz

Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran

Abstract: Landslides are amongst the most damaging natural hazards in mountainous regions. Every year, hundreds of people all over the world lose their lives in landslides; furthermore, there are large impacts on the local and global economy from these events. In this study, landslide hazard zonation in Babaheydar watershed using logistic regression was conducted to determine landslide hazard areas. At first, the landslide inventory map was prepared using aerial photograph interpretations and field surveys. The next step, ten landslide conditioning factors such as altitude, slope percentage, slope aspect, lithology, distance from faults, rivers, settlement and roads, land use, and precipitation were chosen as effective factors on landsliding in the study area. Subsequently, landslide susceptibility map was constructed using the logistic regression model in Geographic Information System (GIS). The ROC and Pseudo-R² indexes were used for model assessment. Results showed that the logistic regression model provided slightly high prediction accuracy of landslide susceptibility maps in the Babaheydar Watershed with ROC equal to 0.876. Furthermore, the results revealed that about 44% of the watershed areas were located in high and very high hazard classes. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the region. 

Keywords: Landslide zonation, multivariate statistical model, Babaheydar watershed, Chaharmahal Va Bakhtiari province.

İran’ın Çaharmahal ve Bahtiyari Bölgesi’nde yer alan Baba Haydar Havzası’nda lojistik regresyon kullanılarak heyelan hassasiyeti haritasının çıkartılması

Özet: Toprak kaymaları, dağlık bölgelerdeki en zarar verici doğal felaketler arasında yer almaktadır. Her yıl, dünyanın dört bir yanında yüzlerce insan toprak kayması neticesinde ölüyor. Ayrıca, bu olayların yerel ve global ekonomi üzerinde de büyük etkileri bulunmaktadır. Bu çalışmada, toprak kayması tehlikesine sahip bölgeleri tespit etmek üzere lojistik regresyon kullanılarak Baba Haydar Havzası’nda toprak kayması tehlikesi haritası çıkartılmıştır. İlk olarak, havadan çekilmiş fotoğraf yorumları ve saha incelemeleri kullanılarak toprak kayması envanter haritası hazırlanmıştır. Bir sonraki adımda rakım, eğim yüzdesi, eğim açısı, litoloji, fay hatlarına olan mesafe, nehirler, yerleşim yerleri ve yollar, arazi kullanımı ve yağış miktarı olmak üzere toprak kaymasına neden olabilecek on adet faktör, çalışma bölgesinde toprak kaymasında etkin faktörler olarak seçilmiştir. Ardından, Coğrafi Bilgi Sisteminde (GIS) lojistik regresyon modeli kullanılarak toprak kayması hassasiyeti haritası oluşturulmuştur. Model değerlendirmesi için ROC ve Pseudo-R2 endeksleri kullanılmıştır. Sonuçlar, lojistik regresyon modelinin, 0.876’lık ROC değeri ile birlikte Baba Haydar Havzası’nda toprak kayması hassasiyet haritasının yüksek bir tahmin doğruluğu sağladığını göstermiştir. Ayrıca sonuçlar, havza bölgelerinin yaklaşık %44’ünün yüksek ve son derece tehlikeli sınıflarda yer aldığını ortaya çıkartmıştır. Sonuç olarak elde edilen toprak kayması hassasiyeti haritaları, uygun havza yönetimi uygulamalarında ve bölgenin sürdürülebilir bir şekilde geliştirilmesinde faydalı olabilir.

Anahtar kelimeler: Heyelan bölgelendirme, çok değişkenli istatistiksel model, Baba Haydar havzası, Çaharmahal ve Bahtiyari bölgesi.

 

Received: 30 June 2014 - Accepted: 20 August 2014

 

To cite this article: Sangchini, E.K., Nowjavan, M.R., Arami, A., 2015. Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran. Journal of the Faculty of Forestry Istanbul University 65(1): 30-40. DOI: 10.17099/jffiu.52751

Anahtar Kelimeler

Landslide zonation, multivariate statistical model, Babaheydar watershed, Chaharmahal Va Bakhtiari province

İran’ın Çaharmahal ve Bahtiyari Bölgesi’nde yer alan Baba Haydar Havzası’nda lojistik regresyon kullanılarak heyelan hassasiyeti haritasının çıkartılması

Öz

Toprak kaymaları, dağlık bölgelerdeki en zarar verici doğal felaketler arasında yer almaktadır. Her yıl,
dünyanın dört bir yanında yüzlerce insan toprak kayması neticesinde ölüyor. Ayrıca, bu olayların yerel ve global
ekonomi üzerinde de büyük etkileri bulunmaktadır. Bu çalışmada, toprak kayması tehlikesine sahip bölgeleri tespit
etmek üzere lojistik regresyon kullanılarak Baba Haydar Havzası’nda toprak kayması tehlikesi haritası çıkartılmıştır.
İlk olarak, havadan çekilmiş fotoğraf yorumları ve saha incelemeleri kullanılarak toprak kayması envanter haritası
hazırlanmıştır. Bir sonraki adımda rakım, eğim yüzdesi, eğim açısı, litoloji, fay hatlarına olan mesafe, nehirler,
yerleşim yerleri ve yollar, arazi kullanımı ve yağış miktarı olmak üzere toprak kaymasına neden olabilecek on adet
faktör, çalışma bölgesinde toprak kaymasında etkin faktörler olarak seçilmiştir. Ardından, Coğrafi Bilgi Sisteminde
(GIS) lojistik regresyon modeli kullanılarak toprak kayması hassasiyeti haritası oluşturulmuştur. Model
değerlendirmesi için ROC ve Pseudo-R2 endeksleri kullanılmıştır. Sonuçlar, lojistik regresyon modelinin, 0.876’lık
ROC değeri ile birlikte Baba Haydar Havzası’nda toprak kayması hassasiyet haritasının yüksek bir tahmin doğruluğu
sağladığını göstermiştir. Ayrıca sonuçlar, havza bölgelerinin yaklaşık %44’ünün yüksek ve son derece tehlikeli
sınıflarda yer aldığını ortaya çıkartmıştır. Sonuç olarak elde edilen toprak kayması hassasiyeti haritaları, uygun havza
yönetimi uygulamalarında ve bölgenin sürdürülebilir bir şekilde geliştirilmesinde faydalı olabilir.

Anahtar Kelimeler

Heyelan bölgelendirme, çok değişkenli istatistiksel model, Baba Haydar havzası, Çaharmahal ve Bahtiyari bölgesi

Kaynakça

• Akgun, A., Turk, N., 2010. Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multi-criteria decision analysis. Environmental Earth Science 61: 595–611.
• Akgun, A., 2012. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9: 93–106
• 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: 15–31.
• Brenning, A., 2005: Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards Earth Systems Science 5(6): 853–862, doi:10.5194/nhess-5-853-2005.
• Bijukchhen, S.M., Kayastha, P., Dhital. M.R., 2013. A comparative evaluation of heuristic and bivariate statistical modeling for landslide susceptibility mappings in Ghurmi-Dhad Khola, east Nepal. Arabian Journal of Geosciences 6(8): 2727-2743.
• Caniani, D. Pascale, S. Sdao, F., Sole, A., 2008. Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards 45: 55–72.
• Clark, W.A.V., Hosking, P.L., 1986. Statistical methods for geographers. Mathematics, 518p.
• 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 51:241–256.
• Eker, R., Aydın, A., 2014. Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yığılca Forest Directorate (Turkey). Turkish Journal of Agricultural Forestry 38(2): 281-290.
• Ermini, L., Catani, F., Casagli, N., 2005. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343.
• Guzzetti, F., Carrara, A., Cardinalli, M., Reichenbach, P., 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31: 181–216.
• Guzzetti, F., 2002. Landslide hazard assessment and risk evaluation: overview, limits and prospective. Proceedings 3rd MITCH Workshop Floods, Droughts and Landslides Who Plans, Who Pays, page 24–26.
• Felicisimo, A., Cuartero, A., Remondo, J., Quiros, E., 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2): 175-189, doi:10.1007/s10346-012-0320-1
• Hasekiogullari, G.D., Ercanoglu, M.A., 2012. New approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Natural Hazards 63(2): 1157-1179, doi:10.1007/s11069-012-0218-1
• Karimi Sangchini, E., Ownegh, M., Sadoddin, A., Mashayekhan, A., 2011. Probabilistic landslide risk analysis and mapping (Case Study: Chehel-Chai watershed, Golestan Province, Iran). Journal of Rangeland Science 2(1): 425-436.
• Kayastha P., Dhital MR, De Smedt, F., 2013. Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal. Journal of the Geological Society of India 81:219-231
• Lee, S., Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4: 33–41.
• Lee, S.T., Yu, T.T., Peng, W.F., Wang, C.L., 2010. incorporating the effects of topographic amplification in the analysis of earthquake-induced landslide hazards using logistic regression. Natural Hazards and Earth System Sciences 10: 2475-2488, doi:10.5194/nhess-10-2475-2010.
• Lee, E.M., Jones, D.K.C., 2004. Landslide risk assessment. Thomas Telford, London, p 454.
• Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94: 379–400.
• Nandi, A., Shakoor, A.A., 2009. GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 110: 11–20.
• Oh, H.J., Lee, S., 2010. Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Advances 3(2): 44–55.
• Pontius, R.J., Schneider, L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems and Environment 85: 239–248.
• Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., 2012a. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards 63: 965–996. doi:10.1007/s11069-012-0217-2.
• Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M., Moradi, H.R., 2013a. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geoscience 6(7): 2351-2365, doi: 10.1007/s12517-012-0532-7.
• Pourghasemi, H.R., Moradi, H.R., Fatemi Aghda, S.M., 2013b. 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.
• Pourghasemi, H.R., Moradi, H.R., Fatemi Aghda, S.M., Gokceoglu, C., Pradhan, B., 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arabian Journal of Geoscience 7(5): 1857-1878, doi: 10.1007/s12517-012-0825-x
• Pouydal C.P., Chang, C., Oh, H.J., Lee, S., 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Science 61: 1049–1064.
• Pradhan, B., 2010a. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances Space Research 45: 1244–1256.
• Pradhan, B., 2011a. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Science 63(2): 329-349, doi:10.1007/s12665-010-0705-1
• Pradhan, B., 2011b. Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environmental and Ecological Statistics 18(3):471–493, doi:10.1007/s10651-010-0147-7
• Pradhan, B., 2011c. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Science 63(2):329–349.
• 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. Computer and Geoscience 51: 350-365, doi:10.1016/j.cageo.2012.08.023
• Pradhan, B. Buchroithner, M.F., 2010. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environmental Engineering Geoscience 16(2): 107–126, doi:10.2113/gseegeosci.16.2.107
• 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. Earth Science Frontiers 14(6):143–152.
• Pradhan, B., Lee, S., 2009. Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Science 3(11):1–15.
• Pradhan, B., Lee, S., 2010a. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Science 60: 1037–1054.
• Pradhan, B., Lee, S., 2010b. Landslide susceptibility assessment and factor effect analysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Modelling and Software 25(6):747–759.
• Pradhan, B., Youssef, A.M., 2010. Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geoscience 3(3): 319–326.
• Pradhan, B., Lee, S., Buchroithner, M.F., 2009. Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Applied Geomatics 1: 3–15.
• Pradhan, B., Lee, S., Buchroithner, M.F., 2010a. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers Environment and Urban Systems 34(3): 216–235.
• Pradhan, B., Sezer, E.A., Gokceoglu, C., Buchroithner, M.F., 2010b. Landslide susceptibility mapping by neuro fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Transactions on Geoscience and Remote Sensing 48(12): 4164–4177.
• Pradhan, B., Youssef, A.M., Varathrajoo, R., 2010c. Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-Spatial Information Science 13(2): 93–102. doi:10.1007/s11806-010-0236-7.
• Pradhan, B., Mansor, S., Pirasteh, S., Buchroithner, M., 2011. Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. International Journal of Remote Sensing 32(14): 4075–4087, doi:10.1080/01431161.2010.484433
• Sakar, S., Kanungo, D.P., 1995. Mehrotar, G.S. Landslide zonation: A case study Garhwal Hymalia, India. Mountain Research and Development 15(4): 301-30.
• Suzen, M.L., Doyuran, V.A., 2004. comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology 45: 665–679.
• Tangestani, M.H., 2009. A comparative study of Demster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. Journal of Asian Earth Science 35: 66–73.
• Yalcin, A., Reis, S., Aydinoglu, A.C., Yomralioglu, T., 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85: 274–287.
• Yesilnacar, E., Topal, T., 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 79: 251–266.
• Yilmaz, I., 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability logistic regression, artificial neural networks, and support vector machine. Environmental Earth Science 61: 821–836.
• Yilmaz, C., Topal, T., Suzen, M.L., 2012. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Science 65: 2161–2178.