Likelihood ratio, logistic regression과 인공신경망을 이용한 산사태 민감도 분석과 검증: 한국 용인지역

Title
Likelihood ratio, logistic regression과 인공신경망을 이용한 산사태 민감도 분석과 검증: 한국 용인지역
Alternative Title
Landslide susceptibility analysis and its verification using likelihood ra-tio, logistic regression and artificial neural network methods: Case study of Yongin, Korea
Author(s)
이사로; 유주형
KIOST Author(s)
Ryu, Joo Hyung(유주형)
Publication Year
2004-07-01
Abstract
The likelihood ratio, logistic regression and artificial neural networks methods are applied and verified for analysis of landslide susceptibility in Yongin, Korea using GIS. From a spatial database containing such data as landslide location, topography, soil, forest, geology and land use, the 14 landslide-related factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression and artificial neural network methods. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the methods. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The verifi-cation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/31801
Bibliographic Citation
9th International Symposium on Landslides, pp.91 - 98, 2004
Publisher
The Joint Technical Committee on Landslides
Type
Conference
Language
English
Publisher
The Joint Technical Committee on Landslides
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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