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Research Article

Predicting Landslide Susceptibility Using High-resolution Active Remote Sensing Data: An Interpretable Machine Learning Approach

고해상도 능동형 원격탐사 자료를 활용한 산사태 민감도 예측: 해석가능한 기계학습 접근

서용훈1, 이건학2†

Yonghun Suh1, Gunhak Lee2†

1서울대학교 지리학과 석사
2서울대학교 지리학과 교수 및 국토문제연구소 겸무연구원
1Master, Department of Geography, Seoul National University
2Professor, Department of Geography and Institute for Korean Regional Studies, Seoul National University
31 August 2024. pp. 89~111
PDF Citation
Abstract
In recent years, increasing extreme weather events have further escalated the risk of landslides. Since the landslide leads to serious loss of human life and property, it is very important to evaluate such risks in advance. The recent advancements in active remote sensing methods have made it easier to access more accurate and detailed surface displacement and rainfall data. However, there have been few works of using such actively remote sensed datasets. Therefore, this study attempts to suggest a machine learning model to predict landslide susceptibility using InSAR (Interferometric SAR) displacement data and HSR (Hybrid Surface Rainfall) estimates as inputs for machine learning models. Moreover, we evaluate the influence of predictor variables for landslide susceptibility systematically by utilizing a SHAP (SHapley Additive exPlanations) model as an interpretable machine learning approach, which has the strength of overcoming a black-box problem with general machine learning models. As a result of case study for Uljin-gun, Gyeongsangbuk-do, XGBoost shows the best predictive performance for landslides and predictor variables such as distance from roads, elevation, maximum daily rainfall intensity, vegetation index, 48 hours accumulated antecedent rainfall, slope, terrain wetness, distance from fault-line, absolute value of surface displacement, and distance from rivers on landslide prediction are identified as key factors for influencing landslide prediction. In particular, rainfall intensity and the absolute value of surface displacement obtained from active remote sensing are contributed to a higher probability of landslide occurrence. This study would be significant in the sense that it presents the applicability of active remote sensed data for landslide susceptibility analysis through the empirical examination. Also, spatially and temporally changing landslide susceptibility based on such actively sensed data could be effectively utilized for landslide monitoring.
Keywords
Landslide Susceptibility Analysis
Active Remote Sensing
Interpretable Machine Learning
Interferometric SAR (InSAR)
Weather Radar
SHAP (SHapley Additive exPlanations)
최근 늘어나고 있는 이상 기상 현상으로 산사태 위험이 점차 증가하고 있다. 산사태는 막대한 인명 피해와 재산 피해를 초래할 수 있기에 이러한 위험을 사전에 평가함은 매우 중요하다. 최근 기술 발전으로 인해 능동형 원격탐사 방법을 사용하여 더 정확하고 상세한 지표 변위 및 강수 데이터를 얻을 수 있게 되었다. 그러나 이러한 데이터를 활용하여 산사태 예측 모델을 개발하는 연구는 찾기 힘들다. 따라서 본 연구에서는 합성개구레이더 간섭법(InSAR)을 사용한 지표 변위 자료와 하이브리드 고도면 강우(HSR) 추정 기법을 통한 강수 정보를 활용하여 산사태 민감도를 예측하는 기계학습 모델을 제시하고 있다. 나아가 기계학습의 블랙박스 문제를 극복할 수 있는 해석가능한 기계학습 방법인 SHAP을 이용하여 산사태 민감도의 영향 변수에 대한 중요도를 체계적으로 평가하였다. 경상북도 울진군을 대상으로 사례 연구를 수행한 결과, XGBoost가 가장 좋은 예측 성능을 보이며, 도로로부터의 거리, 지표 고도, 일 최대 강우 강도, 48시간 선행 누적 강우량, 사면 경사, 지형습윤지수, 단층으로부터의 거리, 경사도, 지표 변위, 하천으로부터의 거리가 산사태 예측에 영향을 미치는 주요 변수로 밝혀졌다. 특히, 능동형 원격탐사를 통해 얻은 자료인 강우 강도와 지표 변위의 절댓값이 높을수록 산사태 발생 확률이 높음을 확인하였다. 본 연구는 능동형 원격탐사 자료의 산사태 민감도 연구에서의 활용 가능성을 실증적으로 보여주고 있으며, 해당 자료를 바탕으로 시공간적으로 변하는 산사태 민감도를 도출함으로써 향후 산사태 민감도 모니터링에 효과적으로 활용될 수 있을 것으로 기대된다.
키워드
산사태 민감도 분석
능동형 원격탐사
해석가능한 기계학습
합성개구레이더 간섭법(InSAR)
기상 레이더
SHAP
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  • Publisher :The Korean Cartographic Association
  • Publisher(Ko) :한국지도학회
  • Journal Title :Journal of the Korean Cartographic Association
  • Journal Title(Ko) :한국지도학회지
  • Volume : 24
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  • DOI :https://doi.org/10.16879/jkca.2024.24.2.089
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