KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Landslide Susceptibility Mapping by Comparing GIS-based Spatial Models in the Java, Indonesia

GIS 기반 공간예측모델 비교를 통한 인도네시아 자바지역 산사태 취약지도 제작

  • 김미경 (연세대학교 건설환경공학과 통합과정) ;
  • 김상필 (연세대학교 건설환경공학과 통합과정) ;
  • 노현주 (연세대학교 건설환경공학과 통합과정) ;
  • 손홍규 (연세대학교 건설환경공학과)
  • Received : 2017.07.18
  • Accepted : 2017.08.08
  • Published : 2017.10.01
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Abstract

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

산사태는 인도네시아에서 오랫동안 피해가 많은 재해로 최근 기후변화와 산지 주위의 무분별한 도시 개발로 인해 위험이 가중되고 있다. 인도네시아 자바지역은 매년 산사태가 빈번하게 발생하고, 인도네시아 인구 절반 이상이 거주하고 있어 그 피해가 크다. 하지만 이러한 위험한 상황에도 불구하고 산사태 위험지역에 매년 거주하는 주민이 증가하고 있어 산사태 위험지역 및 취약지 분석에 대한 기술이 필요한 상황이다. 이에 본 연구는 인도네시아 자바지역을 대상으로 GIS 기반 공간예측모델을 이용하여 산사태 취약성을 평가하고자 한다. 연구지역의 산사태 발생 위치, 지형, 수문, 토양, 토지피복 등의 지형공간정보 자료를 구축하였고, 공간예측모델로는 Weight of Evidence (WoE), 의사결정트리 알고리즘, 인공신경망을 선정하여 산사태 취약지도를 제작하였다. 세 가지 모델은 각각 66.95%, 67.04%, 69.67%의 예측정확도를 보였다. 본 연구의 결과는 향후 인도네시아 산사태 피해 예방 및 산사태 관련 재난관리정책에 중요한 자료로 사용될 수 있을 것으로 기대한다.

Keywords

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