• Title/Summary/Keyword: 재해예측

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Evaluation and Prediction of Failure Factors by Quantification Theory(II) on Banking Slopes in Forest Road (수량화(數量化)II류(類)에 의한 임도(林道) 성토사면(盛土斜面)의 붕괴요인(崩壞要人) 평가 (評價) 및 예측(豫測))

  • Cha, Du Song;Ji, Byoung Yun
    • Journal of Korean Society of Forest Science
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    • v.88 no.2
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    • pp.240-248
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    • 1999
  • On the basis of data obtained from five forest roads collapsed due to a heavy rainfall of 1995 in Chunchon, Kangwon-do, this study was carried out to evaluate and predict the fill slope failure of forest roads with four factors of forest road structure and those of location condition by using Quantification theory(II). The results were summarized as follows ; In the structure factors of forest road, the fill slope failure was mainly occurred in longitudinal gradients less than $2^{\circ}$ or more than $4^{\circ}$, distance of surface-flow longer than 80m, fill slope length greater than 6m, and fill slope gradients steeper than $35^{\circ}$. In the factors of location condition, the failure was mainly occurred in ridge portion of road position, weathered rock and soft rock of constituent material, slope gradients in the range from $35^{\circ}$ to $45^{\circ}$, and concave and convex of longitudinal slope forms. The priority order for factors influencing on fill slope failure was ranked by fill slope length, constituent material, road position, and so on. And the rate of correct discrimination by analysis of fill slope failure was estimated at the high prediction of 86.5%.

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A Research of Risk Assessment for Urethane Fire Based on Fire Toxicity (연소 독성 기반 우레탄 화재의 위험성 평가 연구)

  • Kim, Sung-Soo;Cho, Nam-Wook;Rie, Dong-Ho
    • Fire Science and Engineering
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    • v.29 no.2
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    • pp.73-78
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    • 2015
  • Fire in the risk management subject belongs to high risk disaster which accompanies personnel and materiel loss. So, management of disaster and safety is required to include fire prevention activities, fire risk prediction and investment of safety management expense. Combustion toxicity is required by gas toxicity test (KS F 2271), to minimize human damage. In this study, gas toxicity test were experimented with regard to urethane sample (Depth 5~25 mm) to obtain basic data. Fire effluent exposing to experimental animal were analyzed by FT-IR (Fourier transform infrared spectroscopy). Combustion toxicity index Lethal Fractional Effective Dose ($L_{FED}$) of ISO 13344 was calculated. According to the result of calculating Lethal Concentration 50% ($LC_{50}$) based on $L_{FED}$, $LC_{50}$ of urethane sample containing certain level of fire load is confirmed as $118{\sim}129g/m^3$. Through this study, applicability of this method was confirmed for fire risk assessment. This method can provide information to predict human damage by toxicity combustion gas for securing safety.

Comparative Analysis by Soil Loss and Sediment Yield Analysis Calculation Method of River using RUSLE and GRID (RUSLE와 GRID를 이용한 하천의 토양유실량 및 유사유출량 산정방법별 비교분석)

  • Park, Eui-Jung;Kim, Chul
    • Journal of the Korean Association of Geographic Information Studies
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    • v.10 no.2
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    • pp.112-121
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    • 2007
  • In occasion of soil loss happened in a basin, soil in the near of a stream may flow into the stream easily, but in case that soil is far away from the stream, sediment yield transferred to rivers by rainfall diminishes. To forecast sediment yield of a stream is an essential item for management of basins and streams. Therefore, sediment yield of soil loss produced from a basin is needed to be calculated as accurate as possible. Purpose of the present research is to calculate soil erosion amount in a basin and to forecast sediment yield flowed into a stream by rainfall and analyze sediment yield in the stream. There are various methods that analyze sediment yield of rivers. In the present study, the soil erosion amount was calculated using Revised Universal Soil Loss Equation(RUSLE) and GRID, and sediment yield was calculated using sediment delivery ratio and empirical methods. DEM data, slope of basin, soil map and landuse constructed by GIS were used for input data of RUSLE. The upstream area of the Yeongsan river basin in Gwangju metropolitan city was selected for the study area. Three methods according to the calculation of LS factor were applied to estimate the soil erosion amount. Two sediment delivery ratio methods for the respective methods were applied and, correspondingly, six occasions in sediment yield were calculated. In addition, the above results were compared by relative amount with estimation by the empirical method of Ministry of Construction & Transportation. Sediment yield calculated in the present study may be utilized for the plan, design and management of dams and channels, and evaluation of disaster impact.

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A Study on Condition Analysis of Revised Project Level of Gravity Port facility using Big Data (빅데이터 분석을 통한 중력식 항만시설 수정프로젝트 레벨의 상태변화 특성 분석)

  • Na, Yong Hyoun;Park, Mi Yeon;Jang, Shinwoo
    • Journal of the Society of Disaster Information
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    • v.17 no.2
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    • pp.254-265
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    • 2021
  • Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R2 in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

Correlations of Earthquake Accelerations and LPIs for Liquefaction Risk Mapping in Seoul & Gyeonggi-do Area based on Artificial Scenarios (서울, 경기지역의 시나리오별 액상화 위험지도 작성을 위한 지진가속도와 LPI 상관관계 분석)

  • Baek, Woohyun;Choi, Jaesoon
    • Journal of the Korean GEO-environmental Society
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    • v.20 no.5
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    • pp.5-12
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    • 2019
  • On November 15, 2017, a unpredictable liquefaction damage was occurred at the $M_L=5.4$ Pohang earthquake and after, many researches have been conducted in Korea. In Korea, where there were no cases of earthquake damage, it has been extremely neglectable in preparing earthquake risk maps and building earthquake systems that corresponded to prevention and preparation. Since it is almost impossible to observe signs and symptoms of drought, floods, and typhoons in advance, it is very effective to predict the impacts and magnitudes of seismic events. In this study, 14,040 borehole data were collected in the metropolitan area and liquefaction evaluation was performed using the amplification factor. Based on this data, liquefaction hazard maps were prepared for ground accelerations of 0.06 g, 0.14 g, 0.22 g, and 0.30 g, including 200years return period to 4,800years return period. Also, the correlation analysis between the earthquake acceleration and LPI was carried out to draw a real-time predictable liquefaction hazard map. As a result, 707 correlation equations in every cells in GIS map were proposed. Finally, the simulation for liquefaction risk mapping against artificial earthquake was performed in the metropolitan area using the proposed correlation equations.

Research of Water-related Disaster Monitoring Using Satellite Bigdata Based on Google Earth Engine Cloud Computing Platform (구글어스엔진 클라우드 컴퓨팅 플랫폼 기반 위성 빅데이터를 활용한 수재해 모니터링 연구)

  • Park, Jongsoo;Kang, Ki-mook
    • Korean Journal of Remote Sensing
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    • v.38 no.6_3
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    • pp.1761-1775
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    • 2022
  • Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

Trends in Predicting Groutability Based on Correlation Analysis between Hydrogeological and Rock Engineering Indices: A Review (수리지질 및 암반공학 지수 간 상관분석을 통한 절리암반 내 그라우트 주입성 예측 연구 동향: 리뷰논문)

  • Kwangmin Beck;Seonggan Jang;Seongwoo Jeong;Seungwoo Jason Chang;Minjune Yang
    • The Journal of Engineering Geology
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    • v.33 no.2
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    • pp.307-322
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    • 2023
  • Rock-mass grouting plays a crucial role in the construction of dams and deep caverns, effectively preventing seepage in the foundations, enhancing stability, and mitigating hazards. Most rock grouting is affected by hydrogeological and rock engineering indices such as rock quality designation (RQD), rock mass quality (Q-value), geological strength index (GSI), joint spacing (Js), joint aperture (Ap), lugeon value (Lu), secondary permeability index (SPI), and coefficient of permeability (K). Therefore, accurate geological analysis of basic rock properties and guidelines for grouting construction are essential for ensuring safe and effective grouting design and construction. Such analysis has been applied in dam construction sites, with a particular focus on the geological characteristics of bedrock and the development of prediction methods for grout take. In South Korea, many studies have focused on grout injection materials and construction management techniques. However, there is a notable lack of research on the analysis of hydrogeological and rock engineering information for rock masses, which are essential for the development of appropriate rock grouting plans. This paper reviews the current state of research into the correlation between the grout take with important hydrogeological and rock engineering indices. Based on these findings, future directions for the development of rock grouting research in South Korea are discussed.

Simulation Approach for the Tracing the Marine Pollution Using Multi-Remote Sensing Data (다중 원격탐사 자료를 활용한 해양 오염 추적 모의 실험 방안에 대한 연구)

  • Kim, Keunyong;Kim, Euihyun;Choi, Jun Myoung;Shin, Jisun;Kim, Wonkook;Lee, Kwang-Jae;Son, Young Baek;Ryu, Joo-Hyung
    • Korean Journal of Remote Sensing
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    • v.36 no.2_2
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    • pp.249-261
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    • 2020
  • Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

Vulnerability Assessment of Cultivation Facility by Abnormal Weather of Climate Change (이상기후에 의한 재배시설의 취약성 평가)

  • Yoon, Seong-Tak;Lee, Yong-Ho;Hong, Sun-Hee;Kim, Myung-Hyun;Kang, Kee-Kyung;Na, Young-Eun;Oh, Young-Ju
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.15 no.4
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    • pp.264-272
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    • 2013
  • Climate changes have caused not only changes in the frequency and intensity of extreme climate events, but also temperature and precipitation. The damages on agricultural production system will be increased by heavy rainfall and snow. In this study we assessed vulnerability of crop cultivation facility and animal husbandry facility by heavy rain in 232 agricultural districts. The climate data of 2000 years were used for vulnerability analysis on present status and the data derived from A1B scenario were used for the assessment in the years of 2020, 2050 and 2100, respectively. Vulnerability of local districts was evaluated by three indices such as climate exposure, sensitivity and adaptive capacity, and each index was determined from selected alternative variables. Collected data were normalized and then multiplied by weight value that was elicited in delphi investigation. Jeonla-do and Gangwon-do showed higher climate exposures than the other provinces. The higher sensitivity to abnormal weather was observed from the regions that have large-scale cultivation facility complex compared to the other regions and vulnerability to abnormal weather also was higher at these provinces. In the projected estimation based on the SRES A1B, the vulnerability of controlled agricultural facility in Korea totally increased, especially was dramatic between 2000's and 2020 year.

A Study on Optimal Site Selection for Automatic Mountain Meteorology Observation System (AMOS): the Case of Honam and Jeju Areas (최적의 산악기상관측망 적정위치 선정 연구 - 호남·제주 권역을 대상으로)

  • Yoon, Sukhee;Won, Myoungsoo;Jang, Keunchang
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.18 no.4
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    • pp.208-220
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    • 2016
  • Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.