• Title/Summary/Keyword: 산사태 취약성 평가

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A Study on Stabilization of the Collapsed Slope due to Gyeongju Earthquake at Seokguram Access Road based on Geological Investigation (지질학적 조사를 바탕으로 한 경주지진으로 붕괴된 석굴암 진입도로 비탈면의 안정성 평가에 관한 연구)

  • Kim, Seung-Hyun;Lee, Kwang-Wu
    • Journal of the Korean Geosynthetics Society
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    • v.18 no.4
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    • pp.225-242
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    • 2019
  • Rockfall failure at the access road to Seokguram were occurred due to the earthquake on September 12, 2016. A detailed investigation was carried out in order to find out the cause of the rockfall, to identify the risk of the entire sites, and to prepare proper countermeasure methods and mitigation. We checked for geological and topographical characteristics of overall slopes alongside the access road to Seokguram and made a face map. In addition, we analyzed topographical factors caused by the earthquake through calculating a degree of slope, degree of bearing, upslope contributing area, and wetness index with the use of shading relief map. As a result, we confirmed that the large rockfall occurred with a weak section. In this study, we also evaluated the overall slope stability of the entire access road to Seokguram in order to classify it into danger and caution zones depending on the risk of collapse.

Characterization of Non-structural Flood Mitigation Measures (비구조적 홍수저감대책 고찰)

  • Song, Jae-Ha;Jang, Ho-Yoon;Choi, Hyun-Il;Jee, Hong-Kee
    • Proceedings of the Korea Water Resources Association Conference
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    • 2011.05a
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    • pp.429-429
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    • 2011
  • 우리나라는 연중 강우량의 계절적 편중이 심하여 약 2/3이 6월-9월에 집중하는 기상학적 요인과, 국토의 약 70%가 산악지역으로 되어있는 지형학적 요인 등 홍수에 취약한 자연특성을 갖고 있으며, 특히 하천, 도시 저지대, 해안 및 산지에서는 홍수범람, 내부배제 불량, 해일, 산사태 등으로 매년 많은 인명피해와 재산상의 손실이 크게 발생하고 있다. 또한 최근 발생하고 있는 이상기후 현상과 각종 개발사업으로 인한 불투수면적의 증가 등으로 인해서 극한 홍수의 발생빈도가 높아가고 있으나, 기존 수방시설물의 홍수배제능력 부족 등으로 매년 많은 피해를 입고 있는 실정으로, 구조적인 대책만으로는 재해피해를 경감시키는데 한계가 있음을 인식하여 구조적 대책과 더불어 토지이용규제 및 개발규제, 홍수터관리, 홍수예경보 등 비구조적 재해대 비능력 향상이 시급한 현황이다. 우리나라의 경우, 구조적 수방기술의 발전은 비교적 높은 수준에 도달해 있지만, 구조적 대책에 비해 비구조적 대책의 개발 및 적용은 미흡한 형편이므로, 비구조적 홍수대책의 종합적 정비 및 효율적 운영방법 필요하다. 따라서 본 연구에서는 홍수유형별 발생원인별 대표적인 비구조적 홍수대책을 국내외 적용사례를 조사하여 분석하고, 비구조적인 홍수방어대책들에 대한 장단점 및 적용성 등의 정성적 평가를 실시하였다. 국내 여건에 적합한 홍수위험구역 설정방안을 제시하고자, 국내의 다양한 하천공간 확보를 위한 관련규정인 하천구역, 홍수관리구역, 수변구역, 상수원보호구역, 친수구역, 홍수위험구역, 자연재해위험지구, 방재지구 등에 대하여 조사하였으며, 국외 사례로 영국의 홍수위험구역 평가제도, 미국의 홍수터 관리 프로그램, 호주의 하천공간 분류 기준, 일본의 하천공간 설정 기준 등에 대한 고찰을 수행하였다. 또한 국내 홍수보험 제도의 문제점 분석 및 제고방안을 제시하고자, 현재 소방방재청 주관으로 시행되고 있는 풍수해보험제도에 대한 조사 및 분석을 실시하였으며, 미국에서 시행되고 있는 국가홍수보험프로그램, 프랑스의 자연재해보험, 스위스의 자연재해보험풀 제도, 일본의 홍수보험제도에 대하여 심층적인 고찰을 수행하였다.

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Establishment of Evaluation System for Disaster Resilience Focusing on the Local Road under Complex Disaster (복합재해 발생 예상 시 지방도로 중심의 재난 레질리언스 평가체계 구축)

  • Kim, Young-Hwan;Jun, Kye-Won
    • Journal of Korean Society of Disaster and Security
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    • v.13 no.4
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    • pp.37-46
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    • 2020
  • Although the importance of resilience is emerging around the world, the single definition of resilience related to natural disasters is not clear. The reason for this is that there is no specific definition of how the definition of resilience relates to similar terms such as vulnerability, recovery, adaptability, and sustainability. In addition, it is because each country and region have different geographic and geological characteristics, and each measurement index is different, just as typhoons, droughts, and earthquakes have different types of disasters. Therefore, in this study, the definition of resilience is reflected in the spatial characteristics of this study as the ability to recover from'complex disasters (concentrated heavy rain, landslides, earth and stone flows) occurring on local roads or on local roads adjacent to people or facilities. Defined. And it was divided into DRR: Disaster Resilience focusing on the Road. In addition, domestic and foreign literature surveys were conducted to derive road-centered disaster resilience factors, and a hierarchical structure was established and AHP survey was conducted to establish a DRR evaluation system. As a result of the analysis of the AHP survey, the weight of direct road disaster influencing factors (drainage facilities, protection facilities, etc.) located inside local roads was 0.742, and the weight of indirect road disaster influencing factors (population, property, etc.) located near local roads. Was found to be 0.258, indicating that the direct impact factor of road disaster was relatively higher than that of the indirect impact factor.

A Performance Comparison of Machine Learning Classification Methods for Soil Creep Susceptibility Assessment (땅밀림 위험지 평가를 위한 기계학습 분류모델 비교)

  • Lee, Jeman;Seo, Jung Il;Lee, Jin-Ho;Im, Sangjun
    • Journal of Korean Society of Forest Science
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    • v.110 no.4
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    • pp.610-621
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    • 2021
  • The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.