• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.325-331
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• 2016

This research developed a flood fragility curve of bridges considering the debris impacts. Damage and failures of civil infrastructure due to natural disasters can cause casualties as well as social and economic losses. Fragility analysis is an effective tool to help better understand the vulnerability of a structure to possible extreme events, such as earthquakes and floods. In particular, flood-induced failures of bridges are relatively common in Korea, because of the mountainous regions and summer concentrated rainfall. The main failure reasons during floods are reported to be debris impact and scour; however, research regarding debris impacts is considered challenging due to various uncertainties that affect the failure probability. This study introduces a fragility analysis methodology for evaluating the structural vulnerability due to debris impacts during floods. The proposed method describes how the essential components in fragility analysis are considered, including limit-state function, intensity measure of the debris impact, and finite element model. A numerical example of the proposed fragility analysis is presented using a bridge pier system under a debris impact.

• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.595-604
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• 2005

Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.490-497
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• 2007

A Combined sewer overflows (CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available (which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a continuous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban drainage system used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range $3{\times}DWF$ (dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a decision of storage volume for CSOs reduction and water quality protection.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.23-23
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• 2017

기상레이더는 강우량을 바로 추정하지 못하는 특성으로 인해 정량적 강우산출 과정 중에 다양한 원인으로 인해 불확실성 발생 요소가 존재하나 이를 정량화하고 저감하는데 많은 어려움이 있다. 원인을 살펴보면, 첫째, 기상레이더의 관측에서부터 정량적 강우량 추정까지 일련의 과정에 대한 포괄적으로 불확실성 정량화와 분석이 이루어지지 못하며, 둘째, 전체 불확실성이 어느 정도 되는지 제시하지 못하므로 각 단계별 불확실성이 전체 불확실성 대비 어느 정도 비율이 되는지 제시하지 못한다. 마지막으로 기존 연구들은 불확실성을 줄이고자 여러 방법을 사용하고 있으나 어느 정도 효용성이 있는지 불확실성 측면에서 제시하지 못하고 있다. 따라서 본 연구에서는 Maximum Entropy(ME)와 Uncertainty Delta Method(UMD)를 이용한 접근방법을 제안하여 기상레이더를 활용하여 정량적 강우량을 추정하는 일련의 과정에서 단계별로 불확실성이 어떻게 전파되는지 추정하였다. 본 연구에서는 한반도 전역을 대상으로 2012년 여름철(6~8월)에 발생한 18개 강우사례를 이용하여 품질관리(Open Radar Product Generator 품질관리 알고리즘, fuzzy 알고리즘), 강우추정(Window Probability Matching Method, Marshall-Palmer 관계식), 후처리보정(Local Gauge Correction 기법, Gauge to Radar ratio 기법)단계만을 수행하였으며, 이 결과를 바탕으로 기상레이더 정량적 강우추정 단계별 불확실성을 정량화하였다. 정량화결과, 최종적으로 관측단계의 불확실성보다 최종 불확실성이 줄어들었으나, 강우추정 단계에서 불확실성이 증가하는 것으로 나타났다. 이는 어떤 강우추정식을 적용하느냐에 따라 레이더 강우추정결과가 매우 달라질 수 있음을 의미한다. 따라서 본 연구에서 제시한 불확실성 정량화 방법을 통하여 첫째, 전체 및 단계별 불확실성을 정량화할 수 있고, 둘째, 최종 불확실성 대비 각 단계별 불확실성을 비율을 제시할 수 있으며, 마지막으로 수행단계별로 불확실성 전파과정을 파악할 수 있다. 이는 향후 정량적 레이더 강우추정 과정에 있어서 불확실성을 발생시키는 주요 원인파악과 이에 대한 집중적인 투자를 가능하게 한다. 이러한 과정을 통하여 보다 정확한 정량적 레이더 강우추정이 가능할 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of Climate Change Research
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• v.8 no.2
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• pp.109-123
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• 2017

The effects of recent climate change on hydrological systems could affect the Winter Olympic Games (WOG) because the event is dependent on suitable snow and ice conditions to support elite-level competitions. We investigate the long-term variability and change in winter total precipitation (P), snowfall water equivalent (SFE), and ratios of SFE to P during the period 1973/74~2015/16 in Gangwon province. The climatological percentages of SFE relative to winter total precipitation were 71%, 28%, and 44% in Daegwallyeong, Chuncheon, and Gangneung, respectively. The winter total P, SFE, and SFE/P has decreased (but not significantly), although significant increases of winter maximum and minimum temperature were detected at a 95% confidence level. Notably, a significant negative trend of SFE/P at Daegwallyeong in February, the month of the WOG, was attributable to a larger decrease in SFE related to the increases in maximum and minimum temperature. Winter wet-day minimum temperatures were warmer than climatological minimum temperatures averaged over the study period. The 20-year return values of daily maximum P and SFE decreased in Yongdong area. Since the SFE/P decrease with increasing temperature, the probability of rainfall rather than snowfall can increase if global warming continues.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.187-187
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• 2022

우리나라의 농촌 유역은 크게 1) 상류에 위치한 농업용 저수지, 2) 저수지 방류부, 3) 저수지 하류하천, 4) 하류 농업 지대로 구성된다. 이들 모두 유역의 홍수·침수와 연관되어 있으나 각각의 설계 빈도가 서로 달라 일시에 수용 가능한 수자원의 양이 상이하다. 예컨대 극한 강우가 발생한 경우 PMP를 고려하여 설계된 저수지에서는 유입 홍수량이 통제될 수 있으나 50-200년 빈도로 설계된 하류하천에서는 측면 유입량 때문에 홍수가 발생할 수 있다. 따라서 유역의 홍수 확률을 산출할 때에는 유역 구성지역별 홍수 확률을 산정한 후 종합적으로 고려할 필요가 있다. 특히 농촌유역의 경우 하류하천 및 농경지의 설계 빈도 기준이 도시에 비해 낮아 유역 구성요소 간 처리 가능한 수자원 양의 차이가 크다. 따라서 본 연구에서는 농촌 유역을 대상으로 연구를 진행하였다. 한편, 최근 기후변화로 인해 극한 강우 사상의 빈도가 잦아짐에 따라 유역 내 홍수의 발생이 증가하고 있다. 따라서 기후변화에 따른 미래 농촌 유역의 홍수 발생 여부 파악이 필수적이다. 이에 본 연구에서는 CMIP 6 (Coupled Model Intercomparison Project Phase 6)의 GCM (General Circulation Model) 기상산출물을 농촌 유역에 적용함으로써 미래 농촌 유역의 홍수 발생 여부를 확인하고자 하였다. 또한, CMIP 6의 GCM 산출 기상자료의 시간 단위는 24시간 혹은 3시간으로 시간적 해상도가 낮으므로 유역 홍수 모의를 위하여 GCM 산출물의 시간 분해를 수행하였다. 본 연구에서는 MRC (Multiplicative Random Cascade) 모형을 기후변화 시나리오 기상자료에 적용함으로써 강우 자료의 시간 분해를 수행하고, 시간 분해 결과물을 활용하여 농촌 유역의 미래 홍수 확률을 산정해보고자 하였다. 본 연구의 결과는 향후 농촌 유역의 홍수 확률 산정 기법에 관한 기초 자료로 활용될 수 있을 것으로 사료된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.71-80
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• 2009

Although desire for living without hazardous damages grows these days, threats from natural disasters which we are currently exposed to are quiet different from what we have experienced. To cope with this changing situation, it is necessary to assess the characteristics of the natural disasters. Therefore, the main purpose of this research is to suggest a methodology to estimate the potential property loss and assess the flood risk using a regional regression analysis. Since the flood damage mainly consists of loss of lives and property damages, it is reasonable to express the results of a flood risk assessment with the loss of lives and the property damages that are vulnerable to flood. The regional regression analysis has been commonly used to find relationships between regional characteristics of a watershed and parameters of rainfall-runoff models or probability distribution models. In our research, however, this model is applied to estimate the potential flood damage as follows; 1) a nonlinear model between the flood damage and the hourly rainfall is found in gauged regions which have sufficient damage and rainfall data, and 2) a regression model is developed from the relationship between the coefficients of the nonlinear models and socio-economic indicators in the gauged regions. This method enables us to quantitatively analyze the impact of the regional indicators on the flood damage and to estimate the damage through the application of the regional regression model to ungauged regions which do not have sufficient data. Moreover the flood risk map is developed by Flood Vulnerability Index (FVI) which is equal to the ratio of the estimated flood damage to the total regional property. Comparing the results of this research with Potential Flood Damage (PFD) reported in the Long-term Korea National Water Resources Plan, the exports' mistaken opinions could affect the weighting procedure of PFD, but the proposed approach based on the regional regression would overcome the drawback of PFD. It was found that FVI is highly correlated with the past damage, while PFD does not reflect the regional vulnerabilities.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.