산사태 위험도 분석을 위한 영향인자에 관한 연구 = A Study on Influencing Factors for Landslide Risk Analysis

국문 초록 (Abstract)

본 연구는 산사태 위험도를 체계적으로 평가하고 효과적인 예방 및 관리 전략을 수립하기 위해 델파이 기법을 활용하여 산사태발생에 영향을 미치는 주요 요인들을 식별하고 그 중요도를 평가하였다. 델파이 기법을 통해 3차에 걸친 전문가 집단의 합의를통해 총 18개의 주요 인자가 선정되었으며, 이들은 지형적, 지질학적, 수문학적, 기상학적, 인위적 요인으로 분류되었다.
경사도, 지형 형태, 지하수위, 강우량 등은 산사태 발생 가능성을 높이는 핵심 요인으로 분석되었으며, 특히 지형 및 지질학적요인과 기상 요인이 산사태 발생에 중요한 역할을 하는 것으로 확인되었다. 또한, 인위적 요인 중 토지 이용 변화, 산림벌채, 인공구조물의 설치가 산사태 위험을 증대시키는 주요 요인으로 나타났다. 본 연구는 산사태 위험도 평가를 위한 종합적이고 신뢰성 있는 기초 자료를 제공함으로써, 산사태 발생 가능성을 예측하고 이에 대한 예방 및 대응 전략을 설계하는 데중요한 역할을 할 수 있다. 향후 연구에서는 본 연구에서 도출된 영향인자를 기반으로 한 산사태 위험도 예측 모델을 개발하고, 모델의 정확성과 실효성을 검증하는 것이 필요하다. 더불어, 인자들 간의 상호작용과 복합적인 영향을 고려한 심층 분석을통해 보다 정교한 위험 예측과 맞춤형 대응 전략을 마련할 수 있을 것이다.

다국어 초록 (Multilingual Abstract)

This study systematically evaluates landslide risks and establishes effective prevention and management strategies by utilizing the Delphi method to identify and assess the importance of key factors influencing landslide occurrences. Through three rounds of expert consensus using the Delphi technique, a total of 18 major factors were identified and classified into topographic, geological, hydrological, meteorological, and anthropogenic categories. Factors such as slope, topographic shape, groundwater level, and rainfall amount were analyzed as critical contributors to landslide susceptibility, with topographic, geological, and meteorological factors playing particularly significant roles in landslide occurrence. Additionally, among anthropogenic factors, land use changes, deforestation, and the construction of artificial structures were identified as major contributors to increased landslide risks. This study provides comprehensive and reliable foundational data for landslide risk assessment, enabling the prediction of landslide occurrences and the design of preventive and response strategies. Future research should focus on developing a landslide risk prediction model based on the identified factors and validating the model’s accuracy and effectiveness. Furthermore, in-depth analyses considering the interactions and combined effects of these factors will enable more precise risk forecasting and the development of tailored response strategies.

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