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| Titel |
An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey) |
| VerfasserIn |
M. C. Tunusluoglu, C. Gokceoglu, H. Sönmez, H. A. Nefeslioglu |
| 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 ; 7, no. 5 ; Nr. 7, no. 5 (2007-10-09), S.557-570 |
| Datensatznummer |
250004730
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| Publikation (Nr.) |
 copernicus.org/nhess-7-557-2007.pdf |
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| Zusammenfassung |
| Various statistical, mathematical and artificial intelligence techniques
have been used in the areas of engineering geology, rock engineering and
geomorphology for many years. However, among the techniques, artificial
neural networks are relatively new approach used in engineering geology in
particular. The attractiveness of ANN for the engineering geological
problems comes from the information processing characteristics of the
system, such as non-linearity, high parallelism, robustness, fault and
failure tolerance, learning, ability to handle imprecise and fuzzy
information, and their capability to generalize. For this reason, the
purposes of the present study are to perform an application of ANN to a
engineering geology problem having a very large database and to introduce a
new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the
training stages, total 40 000 training cycles were performed and the minimum
RMSE values were obtained at approximately 10 000th cycle. At this
cycle, the obtained minimum RMSE value is 0.22 for the second training set,
while that of value is calculated as 0.064 again for the second test set.
Using the trained ANN model at 10 000th cycle for the second random
sampling, the debris source area susceptibility map was produced and
adjusted. Finally, a potential debris source susceptibility map for the
study area was produced. When considering the field observations and
existing inventory map, the produced map has a high prediction capacity and
it can be used when assessing debris flow hazard mitigation efforts. |
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