• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.397-397
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• 2015

The frequency of urban floods is recently increased as a consequence of climate change and haphazard development in urban area. To mitigate and prevent the flood damage, we generally utilized a numerical model to investigate the causes and risk of urban flood. Contrary to general flood inundation model simulating only the surface flow, the model needs to consider flow of the sewer network system like SWMM and ILLUDAS. However, this kind of model can not consider the interaction between the surface flow and drainage network. Therefore, we tried to evaluate the impact of bidirectional interaction between sewer and surface flow in urban flooding analysis based on simulations using the quasi-interacted model and the interacted model. As a general quasi-interacted model, SWMM5 and FLUMEN are utilized to analyze the flow of drainage network and simulate the inundation area, respectively. Then, FLO-2D is introduced to consider the interaction between the surface flow and sewer system. The two method applied to the biggest flood event occurred in July 2011 in Sadang area, South Korea. Based on the comparison with observation data, we confirmed that the model considering the interaction the sewer network and surface flow, showed a good agreement than the quasi-interacted model.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.120-128
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• 2011

Korea Groundwater Quality Monitoring Network is a database of annual groundwater quality survey results to prevent groundwater pollution. We estimated contamination index (CI) values for each type of land use, and analyzed temporal trends of pollutant concentration data in the Groundwater Quality Monitoring Network from 2001 to 2009. Among the pollutants considered in the database, the concentrations of nitrate and chloride were higher than their standards. In the case of nitrate, recreation parks, golf courses and general waste dumping regions showed increasing trends according to linear regression analysis, whereas industrial complexes and residential regions of urgan and recreation parks showed increasing trends in the chloride concentration data. According to multiple variable linear regression analysis, EC, pH and topography were major factors influencing CI values. These results suggest that groundwater with a high CI value and increasing trend is vulnerable for potential contamination, which requires more careful groundwater pollution control.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.422-422
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• 2022

최근 기후변화와 도시화 등의 영향으로 인해 전 지구적으로 홍수 피해의 규모와 홍수발생 빈도가 증가하고 있다. 특히, 전 세계 인구의 약 50% 이상이 거주하고 있는 연안지역의 홍수피해 위험성은 급격히 증가하고 있는 추세이며, 각 국가는 홍수 피해를 저감하고 예방하기 위한 노력을 지속적으로 기울이고 있다. 본 연구에서는 연안지역의 감조하천을 대상으로 홍수 예경보 의사결정기법을 개발하고자 하였다. 이를 위해 감조하천에서 관측된 수위는 조석에 의한 수위(조석 성분), 파고에 의한 수위(파고 성분), 강우에 의한 수위(강우-유출 성분), 그리고 잡음에 의한 수위(잡음 성분)의 4가지 수문 성분으로 구성되어 있다고 정의하였고, 감조하천의 예측 강우 성분에 해당하는 예측 수위를 추정하기 위해 수위-유량 관계 곡선식을 개발하고자 하였다. 또한 각 수문 성분별 위기 경보 단계를 설정하고, Bayesian Network를 활용하여 수문 성분들의 위험을 종합적으로 고려할 수 있는 홍수 예·경보 의사결정 기법을 개발하였다. 3가지 난수 발생 방법에 따라 Bayesian Network 모형을 통해 다양한 수문 조건에 따른 조건부 확률을 산정하였으며, 정확도 검토를 수행한 결과 F-1 Socre가 25.1%, 63.5% 및 82.3%의 정확도를 보였다. 향후 본 연구에서 제시한 방법론을 활용한다면 기상청에서 제공하고 있는 예측 강우 및 GRM 모형을 통해 유출량을 산정하고, 이를 예측 수위로 변환하여 연안 지역의 홍수 위험도 매트릭스를 통해 홍수 예·경보에 대한 의사결정을 수행할 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.64-64
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• 2022

이상기후로 인해 돌발적이고 국지적인 호우 발생의 빈도가 증가하게 되면서 짧은 선행시간(~3 시간) 범위에서 수치예보보다 높은 정확도를 갖는 초단시간 강우예측자료가 돌발홍수 및 도시홍수의 조기경보를 위해 유용하게 사용되고 있다. 일반적으로 초단시간 강우예측 정보는 레이더를 활용하여 외삽 및 이동벡터 기반의 예측기법으로 산정한다. 최근에는 장기간 레이더 관측자료의 확보와 충분한 컴퓨터 연산자원으로 인해 레이더 자료를 활용한 인공지능 심층학습 기반(RNN(Recurrent Neural Network), CNN(Convolutional Neural Network), Conv-LSTM 등)의 강우예측이 국외에서 확대되고 있고, 국내에서도 ConvLSTM 등을 활용한 연구들이 진행되었다. CNN 심층신경망 기반의 초단기 예측 모델의 경우 대체적으로 외삽기반의 예측성능보다 우수한 경향이 있었으나, 예측시간이 길어질수록 공간 평활화되는 경향이 크게 나타나므로 고강도의 뚜렷한 강수 특징을 예측하기 힘들어 예측정확도를 향상시키는데 중요한 소규모 기상현상을 왜곡하게 된다. 본 연구에서는 이러한 한계를 보완하기 위해 적대적 생성 신경망(Generative Adversarial Network, GAN)을 적용한 초단시간 예측기법을 활용하고자 한다. GAN은 생성모형과 판별모형이라는 두 신경망이 서로간의 적대적인 경쟁을 통해 학습하는 신경망으로, 데이터의 확률분포를 학습하고 학습된 분포에서 샘플을 쉽게 생성할 수 있는 기법이다. 본 연구에서는 2017년부터 2021년까지의 환경부 대형 강우레이더 합성장을 수집하고, 강우발생 사례를 대상으로 학습을 수행하여 신경망을 최적화하고자 한다. 학습된 신경망으로 강우예측을 수행하여, 국내 기상청과 환경부에서 생산한 레이더 초단시간 예측강우와 정량적인 정확도를 비교평가 하고자 한다.

• Tribology and Lubricants
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• v.20 no.1
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• pp.27-32
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• 2004

This paper presents unsteady oil flow behaviors in the engine lubrication network to clarify the differences between continuous and discontinuous oil supply crankshaft system. Using commercial network analysis program, Flowmaster2, engine lubrication network system analysis were carried out. And effects of crankshaft speed and supplied oil pressure on pressure fluctuation in oil groove and oil flow rate to each bearing were analyzed.

• Journal of Korea Water Resources Association
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• v.39 no.2 s.163
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• pp.89-98
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• 2006

To evaluate the pipe failure impact, current methodologies consider only a broken pipe as the impacted area. However, these approaches are accurate if the broken pipe is the only area isolated from tile system. Depending on the number and locations of on-off valves, more pipes which are adjacent to a broken pipe may be isolated. Using the concept of Segment suggested by Walski, the methodology evaluating the pipe failure impact incorporated with on-off valve locations has been suggested by Jun. However, a segment cannot account for all possible pipe failure impacted areas since it does not consider additional failures, namely the network topological failure and the hydraulic pressure failure. For this reason, a methodology which can consider the network topology and hydraulic pressure limitation as well as on-off valve locations is suggested. The suggested methodology is applied to a real network to verify its applicability As results, it is found that a single pipe failure can affect huge areas depending on the configuration of on-off valves and the network topology. Thus, the applicability of the suggested methodology for evaluating the pipe failure impacts on a water distribution network is proved.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.129-133
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• 2003

The work described in this paper deals with the proposed application of an Artificial Neural Network Model for the Advanced Pressurized Water Reactor APR-1400 transient identification. The approach adopted for testing the network take note of the expectation which should be fulfilled by a network for real-time application, like testing with data in on-line mode and use of actual or real-life patterns for training. The recall test performed demonstrates that use of neural network for transient identification is indeed an attractive preposition.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.241-242
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• 2016

Recently, the interests in safety and prevention from disaster are increasing. In particular, lifeline networks such as water line and sewerage, electricity, gas, and road would be damaged from a disaster. If the lifeline networks do not work in normal, national public service will not properly function. Researches in social network analysis have been conducted for analyzing the interdependency between individuals since 1970s. These network analysis are utilized to investigate a spread of information and disease. However, it is hard to discover the analyzed cases including characteristics of nodes of networks in the area of transportation and disaster. Therefore, this study conducts network analysis of flooded road with flooding scenarios, investigates safe evacuation routes in flooded road network, and suggests efficient approaches for preventing damages from a flooding.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.3
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• pp.287-297
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• 2014

It is important to predict chlorine decay with different water purification processes and distribution pipeline materials, especially because chlorine decay is in direct relationship with the stability of water quality. The degree of chlorine decay may affect the water quality at the end of the pipeline: it may produce disinfection by-products or cause unpleasant odor and taste. Sand filtrate and dual media filtrate were used as influents in this study, and cast iron (CI), polyvinyl chloride (PVC), and stainless steel (SS) were used as pipeline materials. The results were analyzed via chlorine decay models by comparing the experimental and model parameters. The models were then used to estimate rechlorination time and chlorine decay time. The results indicated that water quality (e.g. organic matter and alkalinity) and pipeline materials were important factors influencing bulk decay and sand filtrate exhibited greater chlorine decay than dual media filtrate. The two-component second-order model was more applicable than the first decay model, and it enabled the estimation of chlorine decay time. These results are expected to provide the basis for modeling chlorine decay of different water purification processes and pipeline materials.