Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS)

J. Choi, Y. K. Lee, M. J. Lee, K. Kim, Y. Park, S. Kim, S. Goo, M. Cho, J. Sim, J. S. Won

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages1989-1992
Number of pages4
DOIs
Publication statusPublished - 2011 Nov 16
Event2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
Duration: 2011 Jul 242011 Jul 29

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
CountryCanada
CityVancouver, BC
Period11/7/2411/7/29

Fingerprint

Landslides
Fuzzy inference
landslide
Membership functions
lineament
Soils
Lithology
soil texture
aerial photograph
Geographic information systems
forest soil
field survey
modeling
Wood
lithology
Textures
Polynomials
Antennas

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

Choi, J., Lee, Y. K., Lee, M. J., Kim, K., Park, Y., Kim, S., ... Won, J. S. (2011). Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). In 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings (pp. 1989-1992). [6049518] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2011.6049518
Choi, J. ; Lee, Y. K. ; Lee, M. J. ; Kim, K. ; Park, Y. ; Kim, S. ; Goo, S. ; Cho, M. ; Sim, J. ; Won, J. S. / Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. 2011. pp. 1989-1992 (International Geoscience and Remote Sensing Symposium (IGARSS)).
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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.",
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Choi, J, Lee, YK, Lee, MJ, Kim, K, Park, Y, Kim, S, Goo, S, Cho, M, Sim, J & Won, JS 2011, Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). in 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings., 6049518, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1989-1992, 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, 11/7/24. https://doi.org/10.1109/IGARSS.2011.6049518

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 proceedingConference contribution

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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.

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Choi J, Lee YK, Lee MJ, Kim K, Park Y, Kim S et al. Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS). In 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. 2011. p. 1989-1992. 6049518. (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2011.6049518