C
Candan Gokceoglu
Researcher at Hacettepe University
Publications - 198
Citations - 12456
Candan Gokceoglu is an academic researcher from Hacettepe University. The author has contributed to research in topics: Landslide & Rock mass classification. The author has an hindex of 55, co-authored 186 publications receiving 10355 citations. Previous affiliations of Candan Gokceoglu include Tarbiat Modares University & Eskişehir Osmangazi University.
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A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey
TL;DR: In this paper, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey.
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A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality
TL;DR: A convolutional neural network architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project.
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Assessment of environmental and engineering geological problems for the possible re-use of an abandoned rock-hewn settlement in Urgüp (Cappadocia), Turkey
Resat Ulusay,Candan Gokceoglu,Tamer Topal,Harun Sonmez,Ergün Tuncay,Zeynal Abiddin Erguler,Ozgu Kasmer +6 more
TL;DR: In this paper, an inventory for the openings suggests that there are a number of openings that can be re-used after necessary remedial measures, and the major stability problems threatening the re-use of the openings are structurally-controlled block instabilities, overbreaks, and erosion and shearing of the pillars made of tuff.
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Estimating the uniaxial compressive strength of some clay‐bearing rocks selected from Turkey by nonlinear multivariable regression and rule‐based fuzzy models
TL;DR: The purpose of this study was to construct some predictive models to estimate the uniaxial compressive strength of some clay‐bearing rocks, depending on examination of their slake durability indices and clay contents, and reveals that the fuzzy inference system has a slightly higher prediction capacity than the regression models.
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Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters
TL;DR: The purpose of this study is to apply some statistical and soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties.