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| Titel |
Earthquake-induced landslide-susceptibility mapping using an artificial neural network |
| VerfasserIn |
S. Lee, D. G. Evangelista |
| 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 ; 6, no. 5 ; Nr. 6, no. 5 (2006-07-26), S.687-695 |
| Datensatznummer |
250003703
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| Publikation (Nr.) |
 copernicus.org/nhess-6-687-2006.pdf |
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| Zusammenfassung |
| The purpose of this study was to apply and verify landslide-susceptibility
analysis techniques using an artificial neural network and a Geographic
Information System (GIS) applied to Baguio City, Philippines. The 16 July
1990 earthquake-induced landslides were studied. Landslide locations were
identified from interpretation of aerial photographs and field survey, and a
spatial database was constructed from topographic maps, geology, land cover
and terrain mapping units. Factors that influence landslide occurrence, such
as slope, aspect, curvature and distance from drainage were calculated from
the topographic database. Lithology and distance from faults were derived
from the geology database. Land cover was identified from the topographic
database. Terrain map units were interpreted from aerial photographs. These
factors were used with an artificial neural network to analyze landslide
susceptibility. Each factor weight was determined by a back-propagation
exercise. Landslide-susceptibility indices were calculated using the
back-propagation weights, and susceptibility maps were constructed from GIS
data. The susceptibility map was compared with known landslide locations and
verified. The demonstrated prediction accuracy was 93.20%. |
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