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Candan Gokceoglu

Bio: 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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors used fuzzy logic and analytical hierarchy process (AHP) models to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran.
Abstract: The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (55 landslides) for training the models and the remaining 30 % (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7 %) performed better than AHP (81.1 %) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.

732 citations

Journal ArticleDOI
TL;DR: It is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence of data were taken from the landslide masses, causing imprecise results.

464 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey, which includes five main stages, these being the preparation of landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility maps.
Abstract: Regional landslide susceptibility assessments pose complex problems. To solve these problems, numerous approaches, such as statistical analysis, geotechnical engineering approach, geomorphologic approach and fuzzy logic, have been employed. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability. Minimizing these uncertainties provides realistic approaches. Use of the fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey is the main purpose of the present study. For this purpose, the study includes five main stages, these being the preparation of a landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility map. Slope angle, slope aspect, land use, weathering depth, water conditions and topographical elevation were considered as landslide conditioning factors for the study area. A total of 23 if-then rules was extracted from the field data. Employing these rules, fuzzified index maps representing each parameter were obtained. Finally, combining these maps, the landslide susceptibility map of the area was prepared. When compared with the landslide susceptibility map, the landslides identified in the area were found to be located in the very high- and high-susceptibility zones. As far as the performance of the fuzzy approach for processing is concerned, the images appear to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

420 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the relationship between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, and to develop a general prediction model for the uniaxial compressive strength.

360 citations

Journal ArticleDOI
TL;DR: In this article, the elastic modulus of intact rock is used for many rock engineering projects, such as tunnels, slopes, and foundations, but due to the requirements of high-quality core samples and associated sophisticated test equipment, instead the use of empirical models to obtain this parameter has been an attractive research topic.

357 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.

2,827 citations

Journal ArticleDOI
TL;DR: This paper summarises the interpretation of the Hoek-Brown failure criterion which has been found to work best in dealing with practical engineering problems.

2,813 citations

01 Jan 1995
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system. But it is difficult to implement and time consuming and computationally expensive.
Abstract: 本文对国际科学计量学杂志《Scientometrics》1979-1991年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析,揭示出科学计量学研究的重点、活动的中心及发展趋势,说明了学科带头人在发展科学计量学这门新兴学科中的作用。

1,636 citations

Posted Content
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
Abstract: In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

1,602 citations

Journal ArticleDOI
TL;DR: In this article, the authors outline the principles for landslide mapping, and review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories.

1,290 citations