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Ebru Akcapinar Sezer

Researcher at Hacettepe University

Publications -  92
Citations -  2873

Ebru Akcapinar Sezer is an academic researcher from Hacettepe University. The author has contributed to research in topics: Adaptive neuro fuzzy inference system & Landslide. The author has an hindex of 22, co-authored 90 publications receiving 2324 citations.

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An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm

TL;DR: In this article, landslide susceptibility mapping using a completely expert opinion-based approach was applied for the Sinop (northern Turkey) region and its close vicinity, and an easy-to-use program, ''MamLand,'' was developed for the construction of a Mamdani fuzzy inference system and employed in MATLAB.
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Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia

TL;DR: The neuro-fuzzy model using remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang Valley areas i Malaysia yields reasonable results which can be used for preliminary landuse planning purposes and is a very useful tool for regional landslide susceptibility assessments.
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Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia)

TL;DR: The neuro-fuzzy model using remote-sensing data and geographic information system for landslide susceptibility analysis in a part of the Cameron Highlands areas in Malaysia yields reasonable results, which can be used for preliminary land-use planning purposes.
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Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey

TL;DR: In this article, the authors investigated the possible application of decision tree in landslide susceptibility assessment, and the AUC value of the produced landslide susceptibility map has been obtained as 89.6%.
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Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

TL;DR: This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations for tunnel boring machine performance as a function of rock properties.