• Korean Journal of Hydrosciences
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• v.2
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• pp.85-98
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• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.88-99
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• 2022

The river network is one of the essential topographical characteristics in river management. The river network which as previously constructed by the ground surveying method has recently begun to be efficiently constructed using the remote sensing datasets. Since it is difficult to remove these obstacles such as bridges in the urban rivers, it is rare to construct the urban river networks with the various obstacles. In this study, the Sentinel-2 satellite imagery was used to develop the automatic method for detecting the urban river networks without the obstacles and with the preserved boundaries as follows. First, the normalized difference water index image was generated using the multispectral bands of the given Sentinel-2 satellite imagery, and the binary image that could classify the water body and other regions was generated. Next, the morphological operations were employed for detecting the complete river networks with the obstacles removed and the boundaries preserved. As a result of applying the proposed methodology to Han River in Seoul, the complete river networks with the obstacles removed and the boundaries preserved were well constructed.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 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

• Journal of Korean Society on Water Environment
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• v.20 no.4
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• pp.321-326
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• 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
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• v.5 no.4
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• pp.221-233
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• 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.

• 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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• 2021.06a
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• pp.131-131
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• 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.

• Journal of the Korean Society for Railway
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• v.16 no.4
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• pp.348-351
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• 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
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• v.21 no.5_3
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• pp.723-733
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• 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.

• Journal of Korea Water Resources Association
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• v.31 no.4
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• pp.503-512
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• 1998

When various elements of water balance are displayed at several points of a river network, the runoff amounts at an estuary especially tidal influenced are affected from the elements. This problem can be solved by a model that can generalize and formulate the elements and simulate daily runoff and water requirement. The WWASS model was built using DIROM for the simulation of daily runoff and water requirement, and the water balance elements were modeled to be balanced at the each control point of river network. The model was calibrated, verified and applied to the watershed for the Saemankeum tidal land reclamation development project. It showed that the results from the streamflow simulation at the Mankyung and Dongjin estuary were acceptable for the design of the Saemankeum estuary reservoir.