Abstract
The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagationweights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.
| Original language | English |
|---|---|
| Pages (from-to) | 199-220 |
| Number of pages | 22 |
| Journal | Mathematical Geology |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2006 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Mathematics (miscellaneous)
- Earth and Planetary Sciences (miscellaneous)
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The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. / Lee, Saro; Ryu, Joo Hyung; Lee, Moung Jin; Won, Joong Sun.
In: Mathematical Geology, Vol. 38, No. 2, 01.02.2006, p. 199-220.Research output: Contribution to journal › Article
TY - JOUR
T1 - The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea
AU - Lee, Saro
AU - Ryu, Joo Hyung
AU - Lee, Moung Jin
AU - Won, Joong Sun
PY - 2006/2/1
Y1 - 2006/2/1
N2 - The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagationweights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.
AB - The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagationweights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.
UR - http://www.scopus.com/inward/record.url?scp=33745324560&partnerID=8YFLogxK
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U2 - 10.1007/s11004-005-9012-x
DO - 10.1007/s11004-005-9012-x
M3 - Article
AN - SCOPUS:33745324560
VL - 38
SP - 199
EP - 220
JO - Mathematical Geosciences
JF - Mathematical Geosciences
SN - 1874-8961
IS - 2
ER -