• Korean Journal of Hydrosciences
• /
• 제2권
• /
• pp.85-98
• /
• 1991

This paper is focused on the development of the RIVNET1 model, which is a dynamic wave model, for flood analysis in dendritic river networks with arbitrary cross-sections. This model adopted the $-point implicit RDM and utilized a relaxation algorithim in order to solve the governing equations. The double-sweep method was used to reduce the C.P.U. time to solve the matrix system of the model. This model is applied the analyze flood waves of the Ohid river in the U.S.A. and the Keum river in Korea. The results of analysis obtained from this model are compared with those of the DWOPER and observed data.

• 한국지리정보학회지
• /
• 제25권2호
• /
• pp.88-99
• /
• 2022

하천망은 하천 관리에 있어서 필수적인 지형특성 중 하나이다. 기존에 현장조사를 통해 구축되었던 하천망은 최근에 원격탐사 자료를 활용하여 효율적으로 구축되기 시작하였다. 교량 등 장애물이 많은 도시 하천망의 경우, 하천 내 장애물 제거에 어려움이 있어 온전한 하천망을 구축한 사례는 드물다. 본 연구는 Sentinel-2 위성영상을 활용하여 도시 내 하천에 존재하는 장애물을 제거하고 경계선이 보전된 온전한 하천망을 자동으로 추출하는 기술을 개발하였다. 우선 Sentinel-2 위성영상의 다중분광 밴드를 활용하여 정규수분지수 영상을 제작하고 수체와 그 외 지역을 구분할 수 있는 이진화 영상을 제작하였다. 그리고 모폴로지 연산을 이진화 영상에 적용하여 장애물이 제거되고 경계선이 보전된 온전한 하천망을 추출하였다. 본 연구에서 개발한 기술을 서울시 한강에 적용한 결과, 경계선은 보존되고 교량 등 장애물이 제거된 온전한 하천망을 추출할 수 있었다.

• 상하수도학회지
• /
• 제21권3호
• /
• pp.305-314
• /
• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• 한국물환경학회지
• /
• 제20권4호
• /
• pp.321-326
• /
• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• Journal of Positioning, Navigation, and Timing
• /
• 제5권4호
• /
• pp.221-233
• /
• 2016

This paper deals with performance assessment of the kinematic network-based GPS positioning technique with a view to using it for ellipsoidally referenced bathymetric surveys. To this end, two field trials were carried out on a land vehicle and a surveying vessel. Single-frequency GPS data acquired from these tests were processed by an in-house software which equips the network modeling algorithm with instantaneous ambiguity resolution procedure. The results reveals that ambiguity success rate based on the network model is mostly higher than 99.0%, which is superior to that of the single-baseline model. In addition, achievable accuracy of the technique was accessed at ${\pm}1.6cm$ and 2.7 cm with 95% confidence level in horizontal and vertical component respectively. From bathymetric survey at the West Nakdong River in Busan, Korea, 3-D coordinates of 2,011 points on its bed were computed by using GPS-derived coordinates, attitude, measured depth and geoid undulation. Note that their vertical coordinates are aligned to the geoid, the so-called orthometric height which is widely adopted in river engineering. Bathymetry was constructed by interpolating the coordinate set, and some discussion on its benefit was given at the end.

• 한국정보통신학회논문지
• /
• 제24권2호
• /
• pp.198-203
• /
• 2020

본 논문에서는 소규모 강의 범람 예측을 위한 인공 수위 예측 시스템을 제안한다. 강의 수위 예측은 홍수 피해를 줄일 수 있는 대책이 될 수 있다. 그러나 하천 범람에 영향을 미치는 강 또는 강우의 고유 특성으로 인해 범람 모델을 구축하기가 어렵다. 일반적으로 하류 수위는 상류의 인접한 수위에 영향을 받는다. 따라서 본 연구에서는 측정 지점에서 수위를 예측하기 위해 두 개의 상류 측정 지점의 수위를 순환신경망(LSTM)을 사용하여 인공 지능 모델을 구축했다. 제안 된 인공 지능 시스템은 수위 측정기를 설계하고 Nodejs를 사용하여 서버를 구축했다. 제안 된 신경망 하드웨어 시스템은 실제 강에서 6시간마다 수위를 잘 예측함을 알 수 있었다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2021년도 학술발표회
• /
• pp.131-131
• /
• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• 한국철도학회논문집
• /
• 제16권4호
• /
• pp.348-351
• /
• 2013

The aim of this research is to analyze and periodize the relationship between railroad and river transportation as one aspect of the transformation of the land transportation system in Korea. As a result, three phases can be observed: a first phase of equality and interdependence (1910s); a second phase, subordinating rivers to feeder lines under railroad's dominance; and a third phase when trucks and buses became a major means for local transportation in place of traditional shipping routes. River ports were among the main planned destinations during the first and second phases, but such plans were changed or withdrawn after the third phase. This relationship between river and rail illustrates one geopolitical factor relating to the development of Korea's rail transportation network.

• The Korean Journal of Ecology
• /
• 제21권5_3호
• /
• pp.723-733
• /
• 1998

Restoration to improve the ecological quality of Mt. Nam was explored in a viewpoint of restoration in both landscape and ecosystem levels. A restoration plan in landscape level was based on the result on the land-use pattern in Mt. Nam including its surrounding area and that in ecosystem level on the ecological quality of each landscape element. A plant to construct the green network, which extending from Mt. Nam to the Han river through the Yongsan family park and through the Eungbong urban park was prepared as a restoration project in landscape level to improve the ecological quality of Mt. Nam as an ecological park. On the other hand, a plan for restoration and creation of biotop as a restoration project in ecosystem level was also prepared to improve the ecological quality of each green area consisting green network. Green areas composing green network include keystone green area (Mt. Nam), green stations (Yongsan family park, Eungbong urban park, and the han river citizen's park), and green pathway (or ecological corridor) connecting those green areas.

• 한국수자원학회논문집
• /
• 제31권4호
• /
• pp.503-512
• /
• 1998

여러 소하천으로 이루어지는 수계에서 복잡한 물수지 요소가 여러 지점에서 발생하는 하천 말단 특히 감조지역에 수자원 시설물을 설치하고자 할 때 유입량의 추정이 문제가 되며 물수지 요소의 변동에 따라 말단의 유출량이 영향을 받는다. 이러한 문제는 하천의 유입.유출요소의 정형화를 필요로하며 소유역의 일유출량 추정 모형을 필요로 한다. WWASS 모형은 일별 유입량과 펼요수량 추정 모형으로써 DIROM을 사용하고 있고 물수지 요소들을 하천의 조절점(control point)을 중심으로 처리하도록 되어있다. WWASS 모형을 새만금지구 유역에서 보정 과 검정을 거친 후 만경강과 동진강 하구지점에 적용한 결과 바람직한 결과를 얻을 수 있었다.