İki Farklı Örneklem Tekniği Kullanılarak Oluşturulan Heyelan Duyarlılık Haritalarının Frekans Oranı (FO) Yöntemi ile Karşılaştırılması
Abstract
Keywords
References
- Aditian, A., Kubota, T., Shinohara, Y., 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111. 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. Environmental Geology, 54, 1127–1143. Akgün, A., 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. Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and Environment, 58, 21-44. Althuwaynee, O.F., Pradhan, B., Park, H.J., 2014. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21-36. DOI: 10.1016/ j.catena.2013.10.011. 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. Beguería, S., 2006. Validation and evaluation of predictive models in hazard assessment and risk management. Natural Hazards 37(3), 315–329. Can, A., Dagdelenler, G., Ercanoglu, M., Sonmez, H., 2019. Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bulletin of Engineering Geological Environment, 78, 89-102. Cevik, E., Topal, T., 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology, 44, 949-962. Chen, W., Li, W., Chai, H., Hou, E., Li, X., Ding, X., 2016. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environmental Earth Sciences, 75, 1–14. Chen, W., Pourghasemi, H.R., Panahi, M., Kornejady, A., Wanh, J., Xie, X., Cao, S., 2017. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology, 297, 69-85. Choi, J., Oh, H.-J., Lee, C., Lee, S., 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression and artificial neural network models using ASTER images and GIS, Engineering Geology, 124, 12-23. Clerici, A., Perego, S., Tellini, C., Vescovi, P., 2006. A GIS-Based Automated Procedure for Landslide Susceptibility Mapping by the Conditional Analysis Method: The Baganza Valley Case Study (Italian Northern Apennines). Environmental Geology, 50, 941-961. Conforti, M., Pascale, S., Robustelli, G., Sdao, F., 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (Northern Calabria, Italy). Catena, 113, 236-250.
Details
Primary Language
Turkish
Subjects
Geological Sciences and Engineering (Other)
Journal Section
Research Article
Authors
Gülseren Dağdelenler
*
This is me
0000-0002-9409-8285
Türkiye
Publication Date
June 19, 2020
Submission Date
August 27, 2019
Acceptance Date
October 11, 2019
Published in Issue
Year 2020 Volume: 44 Number: 1
Cited By
Solaklı Havzası’nın (Trabzon) Heyelan Duyarlılığı ve Yerleşim Yeri Risk Analizi
lnternational Journal of Geography and Geography Education
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Çukurova Üniversitesi Mühendislik Fakültesi Dergisi
https://doi.org/10.21605/cukurovaumfd.933874Heyelan Duyarlılığının Analitik Hiyerarşi Süreci (AHS) ve Frekans Oranı (FR) ile Belirlenmesi: Kürk Çayı Havzası (Elazığ) Örneği
Fırat Üniversitesi Sosyal Bilimler Dergisi
https://doi.org/10.18069/firatsbed.1572408Bayes Olasılık Modeli ve Frekans Oranı (FO) Yöntemi ile Esmahanım Deresi Havzası’nın (Düzce) Heyelan Duyarlık Analizi
Turkish Journal of Remote Sensing and GIS
https://doi.org/10.48123/rsgis.1708584