한국방재안전학회논문집 (Journal of Korean Society of Disaster and Security)

  • 제15권4호
  • /
  • Pages.71-78
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  • 2022
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  • 2466-1147(pISSN)
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  • 2508-285X(eISSN)
한국방재안전학회 (Korean Society of Disaster & Security)
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산지재해 위험구간 예측을 위한 토석류 흐름 모의

Movements Simulation of Debris Flow for Prediction of Mountain Disasters Risk Zone

  • 오채연 (강원대학교 방재전문대학원) ;
  • 전계원 (강원대학교 방재전문대학원) ;
  • 강배동 (강원대학교 방재전문대학원)
  • Chae Yeon Oh (Graduate School of Disaster Prevention, Kangwon National University) ;
  • Kye Won Jun (Graduate School of Disaster Prevention, Kangwon National University) ;
  • Bae Dong Kang (Graduate School of Disaster Prevention, Kangwon National University)
  • 투고 : 2022.12.09
  • 심사 : 2022.12.28
  • 발행 : 2022.12.31
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초록

최근 산사태나 토석류와 같은 산지재해가 계류를 따라 흘러와 주거지 및 도로를 덮치는 피해가 증가하고 있으며 본 연구에서는 산지재해의 피해 저감 및 원인 분석을 위하여 토석류 발생지역을 대상으로 현장 조사 및 지상 LiDAR 지형 분석을 수행하고 토석류 수치 모형인 FLO-2D 와 RAMM 모형을 이용하여 토석류 유동 과정을 시뮬레이션하였다. 그리고 토석류 퇴적 면적을 산정하고 실제 발생 구간과 비교 분석하였다. 지상 LiDAR 스캔 자료의 토석류 발생 구간 퇴적 면적은 대략 21,336 m2로 산정되었으며 FLO-2D 모의 결과 20,425 m2, RAMMS 모형의 경우 19,275 m2로 분석되었다. 두 모형 모두 실제 발생 구간과 유사한 형태를 보였다. 구축된 지형자료는 재해 발생 위험지역의 안전성 확보를 위한 기초자료로 활용 가능할 것이다.

Recently, mountain disasters such as landslides and debris flows have flowed along mountain streams and hit residential areas and roads, increasing damage. In this study, in order to reduce damage and analyze causes of mountain disasters, field surveys and Terrestrial LiDAR terrain analysis were conducted targeting debris flow areas, and debris flow flow processes were simulated using FLO-2D and RAMM models, which are numerical models of debris flows. In addition, the debris flow deposition area was calculated and compared and analyzed with the actual occurrence section. The sedimentation area of the debris flow generation section of the LiDAR scan data was estimated to be approximately 21,336 ㎡, and was analyzed to be 20,425 ㎡ in the FLO-2D simulation and 19,275 ㎡ in the case of the RAMMS model. The constructed topographical data can be used as basic data to secure the safety of disaster risk areas.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C1008568).

참고문헌

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