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Frequency Ratio와 Evidential Belief Function을 활용한 산사태 유발에 대한 환경지리적 민감성 분석과 검증 - 2013년 춘천 산사태를 중심으로 -

Analysis and Validation of Geo-environmental Susceptibility for Landslide Occurrences Using Frequency Ratio and Evidential Belief Function – A Case for Landslides in Chuncheon in 2013 –

성효현|안세진|이원영|박선기
한국지형학회 (The Geomorphological Association Of Korea)|2020.03|한국지형학회지 (Journal of The Korean Geomorphological Association)|제27권 제1호|pp. 61-89|DOI 10.16968/JKGA.27.1.61

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참고문헌

키워드

Landslide산사태산사태 민감성 지도환경지리변수빈도비Evidential Belief FunctionLandslide Susceptibility MapGeo-environmental VariablesFrequency Ratio
주제분류지리학

초록

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.

Abstract

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.

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관련 논문AI추천

Mapping Landslide Susceptibility Based on Spatial Prediction Modeling Approach and Quality AssessmentAl-Mamun, JANG, Dong-Ho, Park, Hyun-Su한국지형학회 · 한국지형학회지2019 · 제26권 제3호Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea장동호, Al-Mamun한국지형학회 · 한국지형학회지2016 · 제23권 제2호산사태 분포 예측을 위한 로지스틱, 베이지안, Maxent의 비교알 마문, 박종철, 장동호한국지형학회 · 한국지형학회지2017 · 제24권 제2호Assessment of Landslide Susceptibility Using GIS-Based Models in Jinbu Area장동호, 조상희, Sara Khodadad한국지형학회 · 한국지형학회지2016 · 제23권 제4호공간정보 기반 산사태 발생지역 예측비율 평가고신영, 이근상, 이호준, 조기성한국국토정보공사 · 지적과 국토정보2014 · 제44권 제2호연구지역 규모에 따른 로지스틱 회귀를 이용한 산사태 위험도 평가 결과 비교박형동, 양아란한국자원공학회 · 한국자원공학회지2021 · 제58권 제2호

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