지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이 논문의 연구 히스토리 (5)
2019
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
The objective of this study is to characterize landslide susceptibility depending on various geoenvironmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of ‘very high’ and ‘high’, was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.
목차
등록된 정보가 없습니다.
참고문헌 (45)
참고문헌 신청
Al-Mamun / 2016 / Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea / 한국지형학회지 23(2):109~125
Al-Mamun / 2019 / Mapping Landslide Susceptibility Based on Spatial Prediction Modeling Approach and Quality Assessment / 한국지형학회지 26(3):53~67
Althuwaynee, O. F / 2014 / A novel ensemble bivariate statistical evidential belief function with knowledgebased analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping / Catena 114:21~36
Althuwaynee, O. F / 2012 / Application of an evidential belief function model in landslide susceptibility mapping / Computers & Geosciences 44:120~135
Arabameri, A / 2018 / Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm / Land Degradation and Development 29:4035~4049
Al-Mamun / 2019 / Mapping Landslide Susceptibility Based on Spatial Prediction Modeling Approach and Quality Assessment / 한국지형학회지 26(3):53~67
Althuwaynee, O. F / 2014 / A novel ensemble bivariate statistical evidential belief function with knowledgebased analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping / Catena 114:21~36
Althuwaynee, O. F / 2012 / Application of an evidential belief function model in landslide susceptibility mapping / Computers & Geosciences 44:120~135
Arabameri, A / 2018 / Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm / Land Degradation and Development 29:4035~4049
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea
한국지형학회지
2016 .01
-
Mapping Landslide Susceptibility Based on Spatial Prediction Modeling Approach and Quality Assessment
한국지형학회지
2019 .01
-
Life Risk Assessment of Landslide Disaster in Jinbu Area Using Logistic Regression Model
한국지형학회지
2020 .01
-
Landslide Risk Assessment of Cropland and Man-made Infrastructures using Bayesian Predictive Model
한국지형학회지
2020 .01
-
Assessment of Landslide Susceptibility Using GIS-Based Models in Jinbu Area
한국지형학회지
2016 .12
-
통계적 공간통합 모델을 적용한 산사태 취약성 지도 작성 연구 -왕피천유역 생태⋅경관보전지역을 대상으로-
경관과 지리
2023 .03
-
경기도 광주지역의 극한강우 사상을 고려한 산사태위험도 분석
Crisisonomy
2021 .09
-
매트릭스 기법을 활용한 동적 산사태위험도 평가기법 개발 및 적용성 분석
Crisisonomy
2020 .01
-
지자체의 기후변화 재해 대응 지원을 위한 잠재 산사태 위험지역 분석: 충청 지역을 대상으로
도시연구
2018 .12
-
고해상도 능동형 원격탐사 자료를 활용한 산사태 민감도 예측: 해석가능한 기계학습 접근
한국지도학회지
2024 .08
-
한국의 산사태취약지역과 사방지의 환경요인 분석
국토지리학회지
2015 .06
-
기후변화에 따른 서울시 산사태 관리 개선방안
서울연구원 정책과제연구보고서
2022 .06
-
키르기스스탄 코코메렌강 유역의 대규모 산사태 유형과 지형 발달
한국지형학회지
2019 .01
-
Landslide susceptibility mapping in the Penang Island, Malaysia - Using the AHP and OLS methods -
한국지형학회지
2015 .01
-
산사태 취약지역 정밀점검표 개발
Crisisonomy
2023 .06
-
지진유발산사태(earthquake-induced landslide) 지형을 활용한 고지진 추적 연구
한국지역지리학회 학술대회발표집
2022 .11
-
Landslide susceptibility mapping using GIS-Based models in the some part of Jinbu, Pyeongchang, Kangwon province
대한지리학회 학술대회논문집
2015 .11
-
강원도 춘천 지역의 산사태 발생 강우 임계치 분석
대한지리학회지
2018 .10
-
UAV 기반의 공간정보와 무한사면해석모형을 활용한 산사태 위험도 평가
지적과 국토정보
2015 .01
-
확률론적 모델링 기법을 적용한 산지재해 위험인자 상관성 분석
Crisisonomy
2016 .01
이 논문의 저자 정보
최근 본 자료
댓글(0)
0