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| Titel |
Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks |
| VerfasserIn |
Murat Ercanoglu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 5, no. 6 ; Nr. 5, no. 6 (2005-12-05), S.979-992 |
| Datensatznummer |
250002892
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| Publikation (Nr.) |
 copernicus.org/nhess-5-979-2005.pdf |
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| Zusammenfassung |
| Landslides are significant natural hazards in Turkey, second only to
earthquakes with respect to economic losses and casualties. The West Black
Sea region of Turkey is known as one of the most landslide-prone regions in
the country. The work presented in this paper is aimed at evaluating
landslide susceptibility in a selected area in the West Black Sea region
using Artificial Neural Network (ANN) method. A total of 317 landslides were
identified and mapped in the area by extensive field work and by use of air
photo interpretations to build a landslide inventory map. A landslide
database was then derived automatically from the landslide inventory map. To
evaluate landslide susceptibility, six input parameters (slope angle, slope
aspect, topographical elevation, topographical shape, wetness index, and
vegetation index) were used. To obtain maps of these parameters, Digital
Elevation Model (DEM) and ASTER satellite imagery of the study area were
used. At the first stage, all data were normalized in [0, 1] interval, and
parameter effects on landslide occurrence were expressed using Statistical
Index values (Wi). Then, landslide susceptibility analyses were performed
using an ANN. Finally, performance of the resulting map and the applied
methodology is discussed relative to performance indicators, such as
predicted areal extent of landslides and the strength of relation (rij)
value. Much of the areal extents of the landslides (87.2%) were
classified as susceptible to landsliding, and rij value of 0.85 showed
a high degree of similarity. In addition to these, at the final stage, an
independent validation strategy was followed by dividing the landslide data
set into two parts and 82.5% of the validation data set was found to be
correctly classified as landslide susceptible areas. According to these
results, it is concluded that the map produced by the ANN is reliable and
methodology applied in the study produced high performance, and satisfactory
results. |
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