• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.378-391
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• 2012

The objective of this study was to determine temporal patterns and longitudinal gradients of water chemistry at eight artificial reservoirs and ten streams within the Han-River watershed along the main axis of the headwaters to the downstreams during 2009~2010. Also, we evaluated chemical relations and their variations among major trophic variables such as total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (CHL-a) and determined intense summer monsoon and annual precipitation effects on algal growth using empirical regression model. Stream water quality of TN, TP, and other parameters degradated toward the downstreams, and especially was largely impacted by point-sources of wastewater disposal plants near Jungrang Stream. In contrast, summer river runoff and rainwater improved the stream water quality of TP, TN, and ionic contents, measured as conductivity (EC) in the downstream reach. Empirical linear regression models of log-transformed CHL-a against log-transformed TN, TP, and TN : TP mass ratios in five reservoirs indicated that the variation of TP accounted 33.8% ($R^2$=0.338, p<0.001, slope=0.710) in the variation of CHL and the variation of TN accounted only 21.4% ($R^2$=0.214, p<0.001) in the CHL-a. Overall, our study suggests that, primary productions, estimated as CHL-a, were more determined by ambient phosphorus loading rather than nitrogen in the lentic systems of artificial reservoirs, and the stream water quality as lotic ecosystems were more influenced by a point-source locations of tributary streams and intense seasonal rainfall rather than a presence of artificial dam reservoirs along the main axis of the watershed.

• Korean Journal of Agricultural Science
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• v.30 no.1
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• pp.41-58
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• 2003

This study was conducted to investigate bird community and to suggest a proper way how to manage protect bird community in Gap stream. The survey was carried out over four sections by the line transect method and point counts method from September 2001 to August 2002. Natural stream region as Gasuwon Bridge - Mannyeon Bridge are observed birds were 11 orders 29 families 67 species, Artificial stream region as Mannyeon Bridge - Daedeok Bridge are observed birds were 6 orders 10 families 30 species, Daedeok Bridge - Wonchon Bridge are 8 orders 12 families 28 species, Wonchon Bridge - Gap Stream Bridge are 8 orders 18 families 40 species. All the observed birds in artificial stream region are 8 orders 19 families 47 species. Number of species in natural stream region was higher than artificial stream region owe to a various habitat environment such as forest, cultivated land, streamside forest, sandy plain, gravelly field, reedy field etc. and can not add with the interface and the usage of the human. Number of species in artificial stream region was lower than natural stream region owe to a simple habitat environment and the water ecosystem is severed with embankment block and grass plot with the land ecosystem. The furtherance of various habitat environment which considers the ecosystem like the natural stream as the water ecosystem is joined together with the land ecosystem is desired to attract various wildbirds in Gap stream. The design is desired with the maintenance of the stream to consider the stream corridor which plays ecological important role as connect the fragment habitats.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.71-80
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• 2018

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.328-328
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• 2021

최근 전 지구적인 기후변화의 영향은 강우량의 집중을 야기하며 홍수피해의 규모를 증가시키는 영향을 끼친다. 특히, 아세안 국가들은 해수면 상승, 태풍 및 집중호우에 의한 침수피해 빈발로 최소 2,000만명이 영향을 받고 있다. 국내의 홍수예보모형을 수출하여 아세안 국가에 구축하고 있으나 통신 시설이 불안정하여 중앙제어 방식의 기존의 홍수예보시스템만으로는 긴급상황에 대한 대처가 부족할 수 있다. 따라서 본 연구에서는 하나의 관측소에서 수위, 강우의 관측과 홍수예측, 경보까지 한번에 가능한 관측소를 개발하기 위해 관측된 수위와 강우자료를 활용하여 인공지능기반의 하천수위예측 모형을 개발하였다. 목표 리드타임은 30분에서 6시간으로 설정하였으며 모형은 Tensorflow로 구축하였다. 시계열 자료의 예측에 적합한 LSTM 기법을 적용하였다. 연구의 대상지역은 건설연의 계측시험유역인 설마천유역으로 하였으며 학습에는 2009년부터 2020년까지의 10분 단위 수위 및 강우량자료를 활용하였다. 연구결과 설마천 유역은 규모가 작고 도달시간이 짧아 1시간 후 예측까지는 높은 정확도를 나타냈으나 3시간 이상의 예측결과는 다소 낮게 평가되었다. 다만, 비상상황에서 통신이 두절된 상황에서 위급하게 대피를 위해 홍수경보를 발령하는데는 활용이 가능 할 것으로 판단된다.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.39-48
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• 2020

The main function of artificial wetlands and the largest proportion of the purpose of artificial wetlands created is water purification. The public's interest and demand for water quality increased after the Four major rivers project, and the need for water quality improvement is expected to increase further as the use of waterfront increased due to the improvement of quality of life. Most of the projects focus on only one purpose, and research on the effects of one function is also being analyzed, which undervalues the actual creation of artificial wetlands. Therefore, in order to calculate the comprehensive benefits of artificial wetlands, the effects of flood reduction and water quality improvement were analyzed in this study among the various effects of artificial wetlands along riversides, and the benefits were calculated accordingly. In other words, the effects of flood mitigation and water quality improvement were calculated by comparing the artificial wetlands before and after the construction of artificial wetlands, and the benefits of each of them were calculated.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.198-203
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• 2020

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.159-159
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• 2017

효율적인 물 관리를 위해서는 하천 유량 파악이 필수적이지만, 경제적 이유 등으로 인하여 현장에서 정확한 유량 자료를 꾸준히 확보하는 데에는 한계가 있다. 본 연구에서는 이러한 문제점을 극복하고자 SAR 영상을 이용하여 하천의 수위와 유량을 추정하였다. SAR 영상 자료는 악천후 및 주야의 영향을 받지 않는 ESA(European Space Agency)의 Sentinel-1 영상을 이용하였다. 위성자료에서 하천의 면적을 추출한 후 수위 및 유량과의 상관관계를 분석하였다. 촬영 시간 등에 의한 위성 영상의 조도 차이에 따른 하천 면적의 오차를 제거하기 위하여 영상을 보정하였고 주변 지역에 의한 오차를 줄이기 위하여 하천유역을 분리하여 면적을 추출하였다. 이를 통해 하천 면적과 수위 및 유량의 상관관계를 파악하였다. 국내 10여 개의 하천에 대하여 기법을 적용한 결과, 수위와 유량을 비교적 정확히 추정할 수 있었다. 본 연구의 결과는 미계측 유역의 수자원 관리 능력을 향상시킬 것으로 기대된다.

• Journal of the Korean association of regional geographers
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• v.14 no.2
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• pp.105-113
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• 2008

The purpose of this paper is to analyze the influences of extreme flood on urban stream's habitat environment at Shincheon stream in Daegu. In case of Shincheon stream, as any extreme floods have not flowed over the artificial bank, an extreme flood have an effects on inner part of confined channel. To make riparian park along Shincheon channel, Concrete and rubber weirs are constructed. These weirs interrupted flow of running water as obstacles during extreme flood, and running water moved aside into and destructed banks of lower-flow-channel. In reach of no weir, as all small-scale topographic bedforms were eliminated, habitat environment of river ecosystem was simplified, and biodiversity of river ecosystem was decreased. As simplified riverbed become irregular bedforms through frequent small-scale-floods, river ecosystem will become vigorous.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.562-562
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• 2016

신경망 모형에서 학습이란 주어진 입출력시스템에 대하여 원하는 동작을 수행할 수 있도록 연결 강도를 최적의 상태로 적응(adaptation)시키는 과정을 의미한다. 따라서 강수와 지하수위의 관계를 연계시킨 인공신경망기법은 선택적으로 예측 지하수위에 영향을 미치는 변수들을 학습에 의하여 택함으로써 예측모형을 구성할 수 있다. 즉, 예측 지하수위와의 상관관계에 의하여 입력되는 변수와의 연결강도를 조정하여 매개변수 조정 및 모형의 최적화를 자동화할 수 있다. 본 연구에서는 지하수위에 영향을 주는 요소는 지하수위와 강우량이라고 가정하고, 지하수위의 입출력과정을 시계열 분석에 의하여 모형화하였으며 예측지하수위는 강우 및 지하수위의 선행조건과 매우 밀접한 관계를 갖는다. 따라서 선행강우 및 지하수위의 상태에 따라 이를 입력하여 미래의 지하수위를 예측하게 된다. 이 모형을 제주지역의 관측소에 적용한 결과 관측소별로 타당한 예측결과를 도출하였다.