Abstract
This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil, forest and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs), and analysis results were verified by using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping.
| Original language | English |
|---|---|
| Title of host publication | 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings |
| Pages | 1989-1992 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2011 Nov 16 |
| Event | 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada Duration: 2011 Jul 24 → 2011 Jul 29 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|
Other
| Other | 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 11/7/24 → 11/7/29 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)
Cite this
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Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). / Choi, J.; Lee, Y. K.; Lee, M. J.; Kim, K.; Park, Y.; Kim, S.; Goo, S.; Cho, M.; Sim, J.; Won, J. S.
2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. 2011. p. 1989-1992 6049518 (International Geoscience and Remote Sensing Symposium (IGARSS)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS)
AU - Choi, J.
AU - Lee, Y. K.
AU - Lee, M. J.
AU - Kim, K.
AU - Park, Y.
AU - Kim, S.
AU - Goo, S.
AU - Cho, M.
AU - Sim, J.
AU - Won, J. S.
PY - 2011/11/16
Y1 - 2011/11/16
N2 - This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil, forest and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs), and analysis results were verified by using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping.
AB - This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil, forest and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs), and analysis results were verified by using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping.
UR - http://www.scopus.com/inward/record.url?scp=80955132685&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80955132685&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2011.6049518
DO - 10.1109/IGARSS.2011.6049518
M3 - Conference contribution
AN - SCOPUS:80955132685
SN - 9781457710056
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1989
EP - 1992
BT - 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
ER -