Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
National level landslide susceptibility assessment of Turkey utilizing public domain dataset
Date
2016-05-01
Author
Okalp, Kivanc
Akgün, Haluk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
242
views
0
downloads
Cite This
Landslide studies have been integrated into geographic information systems with the help of technological developments using several methods like inventory, heuristic, statistic and deterministic methods in the recent years. However, since a nationwide landslide susceptibility zoning map has not been produced for the entire territory of Turkey, this study aims to produce a landslide susceptibility map of Turkey at a national scale by utilizing publicly available datasets. In order to develop a landslide susceptibility map of Turkey at the scale of 1: 2,000,000, an index-based calculation, which considers six factors (slope, lithology, local relief, rainfall, land use, seismicity) that covers the entire territory of Turkey and controls the occurrence of landslides, was applied in a 500 x 500 m pixel resolution. Each layer (factor) having various effects on landslide susceptibility has been merged into the model with assigned weights. Four different weight groups were assigned to the layer sets through expert judgement in order to capture the layer variability for landslide susceptibility in Turkey. The performances of four different weight groups were compared and evaluated by using a receiver operator characteristics curve for minimizing the uncertainty of expert judgement procedure. It was observed that the W-3 group was superior to the other weight groups in prediction skills. The susceptibility map of W3 has been classified into five groups: no, low, moderate, high and very high susceptibility. The no susceptibility class represents 4.2 % of the Turkish territory (plains and low hills), low susceptibility class 36.4 %, medium susceptibility 8.3 %, high susceptibility 47.5 % and very high susceptibility class 3.6 %, mostly in the western and middle Black Sea regions, respectively.
Subject Keywords
Small scale
,
Large datasets
,
Country-wide
,
Index mapping
,
Index mapping
,
Qualitative analysis
URI
https://hdl.handle.net/11511/40652
Journal
ENVIRONMENTAL EARTH SCIENCES
DOI
https://doi.org/10.1007/s12665-016-5640-3
Collections
Department of Geological Engineering, Article
Suggestions
OpenMETU
Core
Uncertainty models for vector based functional curves and assessing the reliability of G-Band
Kurtar, Ahmet Kürşat; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2006)
This study is about uncertainty medelling for vector features in geographic information systems (GIS). It has mainly two objectives which are about the band models used for uncertainty modelling . The first one is the assessment of accuracy of GBand model, which is the latest and the most complex uncertainty handling model for vector features. Some simulations and tests are applied to test the reliability of accuracy of G-Band with comparing Chrisman’s epsilon band model, which is the most frequently used b...
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Investigating sentimental relation between social media presence and academic success of Turkish Universities
Gunduz, Sedef; Demirhan, Fatih; SAĞIROĞLU, Şeref (2014-12-06)
In this study an approach that uses social networking data for developing sentiment analysis system is proposed. With the help of developed software, it is tried to find out whether there is any relation between universities' academic success and sentiment of the public about them in social media. After collecting enough text based data from Twitter, preprocessing of data is carried out and final data is trained by means of Naive Bayes Classifier. After testing process, experimental results have shown that ...
Landslide susceptibility assessment in medium-scale: case studies from the major drainage basins of Turkey
Okalp, Kıvanç; Akgün, Haluk (2022-04-01)
Inventory, heuristic, statistical, and deterministic methods have been widely used in landslide susceptibility studies in recent years. This study aims to apply a GIS-based semi-quantitative approach (Analytical Hierarchy Process-AHP) to assess landslide susceptibility and determine landslide-prone areas at a regional level with medium-scale using publicly available datasets. The AHP was preferred due to its ability of correlating different parameters, which aids the researchers in producing relatively cons...
PROGRESSIVE CLUSTERING OF MANIFOLD-MODELED DATA BASED ON TANGENT SPACE VARIATIONS
Gokdogan, Gokhan; Vural, Elif (2017-09-28)
An important research topic of the recent years has been to understand and analyze manifold-modeled data for clustering and classification applications. Most clustering methods developed for data of non-linear and low-dimensional structure are based on local linearity assumptions. However, clustering algorithms based on locally linear representations can tolerate difficult sampling conditions only to some extent, and may fail for scarcely sampled data manifolds or at high-curvature regions. In this paper, w...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
K. Okalp and H. Akgün, “National level landslide susceptibility assessment of Turkey utilizing public domain dataset,”
ENVIRONMENTAL EARTH SCIENCES
, pp. 0–0, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40652.