Landslide flow path modellingA Case Study on AranayakaLandslide

산사태 유로 모델링 : Aranayaka 산사태 사례 연구

Authors:

Malithi De Silva : University of Kelaniya

N.M.T De Silva
University of Colombo School of Computing
2018

Abstract

산사태가 발생하기 쉬운 구릉 지역 근처에서 발생하는 최근 인구 증가 및 개발은 취약성을 증가시킵니다. 기후 변화의 영향은 산사태 위험의 가능성을 더욱 높입니다. 따라서 인명 및 재산 피해를 방지하기 위해서는 불안정한 경사면 거동에 대한 적절한 관찰과 분석이 중요합니다.

산사태 흐름 경로 예측은 산사태 흐름 경로를 결정하는 데 중요하며 위험 매핑의 필수 요소입니다. 그러나 현상의 복잡한 특성과 관련 매개변수의 불확실성으로 인해 흐름 경로 예측은 어려운 작업입니다. 이 작업에서는 Kegalle 지역의 Aranayaka 지역의 주요 산사태 사고를 흐름 경로를 모델링하기 위한 사례 연구로 사용합니다.

위치에서 디지털 고도 모델을 기반으로 잠재적 소스 영역이 식별되었습니다. 확산 영역 평가는 D8 및 다중 방향 흐름 알고리즘이라는 두 가지 흐름 방향 알고리즘을 기반으로 했습니다. 이 프로토타입 모델을 사용하여 사용자는 슬라이드의 최대 너비, 런아웃 거리 및 슬립 표면적과 같은 산사태 관련 통계를 대화식으로 얻을 수 있습니다.

모델에서 얻은 결과는 실제 Aranayaka 산사태 데이터 세트와 해당 지역의 산사태 위험 지도와 비교되었습니다. D8 알고리즘을 사용하여 구현된 도구에서 생성된 산사태 흐름 경로는 65% 이상의 일치를 나타내고 다중 방향 흐름 알고리즘은 실제 흐름 경로 및 기타 관련 통계와 69% 이상의 일치를 나타냅니다.

또한, 생성된 유동 경로 방향과 예상되는 산사태 시작 지점이 실제 산사태 경계 내부에 잘 일치합니다.

Recent population growth and developments taking place close to landslides prone
hilly areas increase their vulnerability. Climate change impacts further raise the
potential of landslide hazard. Therefore, to prevent loss of lives and damage to
property, proper observation and analysis of unstable slope behavior is crucial.
Landslide flow path forecasting is important for determining a landslide flow route and
it is an essential element in hazard mapping. However, due to the complex nature of
the phenomenon and the uncertainties of associated parameters flow path prediction is
a challenging task.
In this work, the major landslide incident at Aranayaka area in Kegalle district is taken
as the case study to model the flow path. At the location, potential source areas were
identified on the basis of the Digital Elevation Model. Spreading area assessment was
based on two flow directional algorithms namely D8 and Multiple Direction Flow
Algorithm. Using this prototype model, a user can interactively get landslide specific
statistics such as the maximum width of the slide, runout distance, and slip surface area.
Results obtained by the model were compared with the actual Aranayaka landslide data
set the landslide hazard map of the area.
Landslide flow paths generated from the implemented tool using D8 algorithm shows
more than 65% agreement and Multiple Direction Flow Algorithm shows more than
69% agreement with the actual flow paths and other related statistics. Also, the
generated flow path directions and predicted possible landslide initiation points fit
inside the actual landslide boundary with good agreement.

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