• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.425-437
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• 2013

This study performed the bivariate drought frequency analysis for duration and severity of drought, using copula functions which allow considering the correlation structure of joint features of drought. We suggested the confidence intervals of duration-severity-frequency (DSF) curves for the given drought duration using stochastic scheme of monthly rainfall generation for 57 sites in Korea. This study also investigated drought risk via illustrating the largest drought events on record over 50 and 100 consecutive years. It appears that drought risks are much higher in some parts of the Nakdong River basin, southern and east coastal areas. However, such analyses are not always reliable, especially when the frequency analysis is performed based on the data observed over relatively short period of time. To quantify the uncertainty of drought frequency curves, the droughts were filtered by different durations. The 5%, 25%, 50%, 75%, and 95% confidence intervals of the drought severity for a given duration were estimated based on the simulated rainfall time series. Finally, it is shown that the growing uncertainties is revealed in the estimation of the joint probability using the two marginal distributions since the correlation coefficient of two variables is relatively low.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.31-38
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• 2014

Seismic of the Korean Peninsula is terrible about 1,900 times. Lately, because of a world-occurring seismic, investment of seismic design about ground & structures come to the force as means to protect national life and property. This study evaluated having seismic performance above design criteria among the existing port structures not applied seismic design. Based on the results, classified apprehensive area of liquefaction from seismic performance evaluation and made hazards according to liquefaction risk & structural performance using the analysis of seismic performance and GIS method. After Establishing quantitative & detailed input database through liquefaction evaluation and seismic performance, analysed all seismic data are used directly valuation data on repair reinforcement for apprehensive area of earthquakes.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.1-11
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• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Journal of the Korea Institute of Building Construction
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• v.9 no.6
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• pp.91-98
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• 2009

One role of waterproofing work is to block external moisture and water. Defects in waterproofing work in building construction brings on huge repair costs for related construction work as well as for the waterproofing layer itself. However, we don't have a quantitative probabilistic management method for waterproofing work to successfully anticipate and prevent defects. From an analysis of the literature and prior research, defects in the waterproofing work in the underground parts of buildings occur frequently. We selected Fluid-Applied Membrane waterproofing work as representing waterproofing work in the underground parts of a building, and researched the general types and causes of defects. In this study, we developed the Relative-FMEA (Failure Mode and Effect Analysis) approach that merges the Matrix method and FMEA. From a survey of experts, we deduced the most important management factors for Fluid-Applied Membrane waterproofing work for the underground parts of buildings.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.137-143
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• 2003

The prevention of marine accidents has been a important topic in marine society for long time, and various safety policies and countermeasures have been developed and applied to prevent those accidents. In spite of these efforts, however, significant marine accidents have taken place intermittently. Ship is being operated under a highly dynamic environments, and many factors are related with ship's collision, whose factors are interacting. So, the analysis on ship's collision causes are very important to prepare countermeasures which will ensure the safe navigation. This study analysed the ship's collision data over the past 10 years(1991-2000), which is compiled by Korea Marine Accidents Inquiry Agency. The analysis confirmed that‘ship's collision’is occurred most frequently and the cause is closely related with human factor. The main purpose of this study is to analyse human factor. For this, the structure of human factor is analysed by the questionnaire methodology. Marine experts were surveyed based on major elements that were extracted from the human factor affecting to ship's collision. FSM has been widely adopted in modeling a dynamic system which is composed of human factors. Then, the structure analysis on the causes of ship's collision using FSM are performed. This structure model could be used in understanding and verifying the procedure of real ship's collision. Furthermore it could be used as the model to prevent ship's collision and reduce marine accidents.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.1
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• pp.31-38
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• 2018

Earthquake disasters that exceed the design criteria can pose significant threats to nuclear facilities. Seismic probabilistic safety assessment(PSA) is a probabilistic way to quantify such risks. Accordingly, seismic PSA has been applied to domestic and overseas nuclear power plants, and the safety of nuclear power plants was evaluated and prepared against earthquake hazards. However, there were few examples where seismic PSA was applied in case of a research reactor with a relatively small size compared to nuclear power plants. Therefore, in this study, seismic PSA technique was applied to actually completed research reactor to analyze its safety. Also, based on these results, the optimization study on the seismic capacity of the system constituting the research reactor was carried out. As a result, the possibility of damage to the core caused by the earthquake hazard was quantified in the research reactor and its safety was confirmed. The optimization study showed that the optimal seismic capacity distribution was obtained to ensure maximum safety at a low cost compared with the current design. These results, in the future, can expect to be used as a quantitative indicator to effectively improve the safety of the research reactor with respect to earthquakes.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.446-453
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• 2020

By applying the Analytic Hierarchy Process(AHP) method, we calculated the weight of factors related to the selection of check dam sites and generated basic data related to the selection of the target site. As a result of the AHP classification, three evaluation items(Susceptibility, Vulnerability, and Constructability), 12 evaluation factors (Susceptibility 10, Vulnerability, and Constructability 1), and two-six evaluation criteria in each evaluation factor were selected. After calculating the relative importance by using the selected evaluation items, evaluation factors, and criteria, the vulnerability items such as medical facilities, facilities for the aged, educational facilities, and private homes were found to be higher than the other items. When the relative weight for deciding qualified sites was applied to the forest watersheds in the Gangwon region, 8,601 forest watersheds(35.2 % of the total forest watersheds) were found to be suitable for the installation of check dams.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.485-492
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• 2014

In regards to natural disasters, vulnerability analysis is a component of the disaster risk analysis with one of its objectives as a basis for planning priority setting activities. The volcano eruption raises many casualties and property in the surrounding area, especially when the volcano located in densely populated areas. Volcanic eruptions cannot be prevented, but the risk and vulnerability can be reduced which involve careful planning and preparations that anticipate a future crisis. The social vulnerability as social inequalities with those social factors can influence the susceptibility of various groups to harm and govern their ability to respond. This study carried out the methods of Social Vulnerability Index (SoVI) to measure the socially created vulnerability of the people living in Merapi proximal hamlets in Central Java, Indonesia that refers to the socioeconomic and demographic factors that affect the resilience of communities in order to describe and understand the social burdens of risk. Social vulnerability captured here, using a qualitative survey based-data such as interviews to local people with random ages and background to capture the answer vary, also interviews to stakeholders to help define social vulnerability variables. The paper concludes that by constructing the vulnerability index for the hamlets, the study reveals information about the distribution and causes of social vulnerability. The analysis using SoVI confirms that this method works well in ensuring that positive values indicating high social vulnerability and vice versa.