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| Titel |
Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera |
| VerfasserIn |
F. Sdao, D. S. Lioi, S. Pascale, D. Caniani, I. M. Mancini |
| 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 ; 13, no. 2 ; Nr. 13, no. 2 (2013-02-15), S.395-407 |
| Datensatznummer |
250017576
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| Publikation (Nr.) |
 copernicus.org/nhess-13-395-2013.pdf |
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| Zusammenfassung |
| The complete assessment of landslide susceptibility needs uniformly
distributed detailed information on the territory. This information, which
is related to the temporal occurrence of landslide phenomena and their
causes, is often fragmented and heterogeneous. The present study evaluates
the landslide susceptibility map of the Natural Archaeological Park of
Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The
assessment of the degree of "spatial hazard" or "susceptibility" was
carried out by the spatial prediction regardless of the return time of the
events. The evaluation model for the susceptibility presented in this paper
is very focused on the use of innovative techniques of artificial
intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network.
The method described in this paper is a novel technique based on a
neuro-fuzzy system. It is able to train data like neural network and it is
able to shape and control uncertain and complex systems like a fuzzy system.
This methodology allows us to derive susceptibility maps of the study area.
These data are obtained from thematic maps representing the parameters
responsible for the instability of the slopes. The parameters used in the
analysis are: plan curvature, elevation (DEM), angle and aspect of the
slope, lithology, fracture density, kinematic hazard index of planar and
wedge sliding and toppling. Moreover, this method is characterized by the
network training which uses a training matrix, consisting of input and
output training data, which determine the landslide susceptibility. The neuro-fuzzy method
was integrated to a sensitivity analysis in order to overcome the
uncertainty linked to the used membership functions. The method was compared
to the landslide inventory map and was validated by applying three methods:
a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a
SCAI method. The developed neuro-fuzzy method showed a good performance in
the determination of the landslide susceptibility map. |
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