 |
| Titel |
Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model |
| VerfasserIn |
M. J. García-Rodríguez, J. A. Malpica |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 10, no. 6 ; Nr. 10, no. 6 (2010-06-25), S.1307-1315 |
| Datensatznummer |
250008237
|
| Publikation (Nr.) |
 copernicus.org/nhess-10-1307-2010.pdf |
|
|
|
|
|
| Zusammenfassung |
This paper presents an approach for assessing earthquake-triggered landslide
susceptibility using artificial neural networks (ANNs). The computational
method used for the training process is a back-propagation learning
algorithm. It is applied to El Salvador, one of the most seismically active
regions in Central America, where the last severe destructive earthquakes
occurred on 13 January 2001 (Mw 7.7) and 13 February 2001 (Mw 6.6). The first one triggered more than 600 landslides (including the
most tragic, Las Colinas landslide) and killed at least 844 people.
The ANN is designed and programmed to develop landslide susceptibility
analysis techniques at a regional scale. This approach uses an inventory of
landslides and different parameters of slope instability: slope gradient,
elevation, aspect, mean annual precipitation, lithology, land use, and
terrain roughness. The information obtained from ANN is then used by a
Geographic Information System (GIS) to map the landslide susceptibility. In a
previous work, a Logistic Regression (LR) was analysed with the same
parameters considered in the ANN as independent variables and the occurrence
or non-occurrence of landslides as dependent variables. As a result, the
logistic approach determined the importance of terrain roughness and soil
type as key factors within the model. The results of the landslide
susceptibility analysis with ANN are checked using landslide location data.
These results show a high concordance between the landslide inventory and the
high susceptibility estimated zone. Finally, a comparative analysis of the
ANN and LR models are made. The advantages and disadvantages of both
approaches are discussed using Receiver Operating Characteristic (ROC)
curves. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|