• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.423-436
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• 2003

The objective of this study is to propose an automatic parameter estimation scheme considering runoff components of Tank model. It estimates model parameters by Powell's automatic algorithm based on the runoff component separation of the observed hydrograph by using digital filter method. The selected study areas are the 4 main dam sites on the Han River. The simulated flows are compared with the observed flows depending on whether runoff component consideration or not. As a result, the estimated model parameters from classical Powell's method only can relatively well simulate the time variation of total runoff, but gives poor runoff component simulations. Therefore, it can be concluded that the proposed automatic parameter estimation scheme in this study Is more reliable and objective.

• Journal of Soil and Groundwater Environment
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• v.24 no.5
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• pp.17-30
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• 2019

Intensive agricultural development in Mexicali valley, Baja-California, Mexico, has induced tremendous strain on the limited water resources. Agricultural water consumption in the valley mainly relies on diversions of the Colorado River, but their water supply is far less than the demand. Hence, the use of groundwater for irrigation purposes has gained considerable attention. To account for these changes, it is important to evaluate surface water and groundwater conditions based on historical water use. This study identified the effects of agricultural activities on groundwater levels and groundwater recharge in the Mexicali valley (in irrigation unit 16) by a comprehensive MODFLOW Farm process (MF-FMP) numerical modeling. The MF-FMP modeling results showed that the water table in the study area is drawn downed, more in eastern areas. The inflow-outflow analysis demonstrated that recharge to the aquifer occurs in response to agricultural supplies. In general, the model provides MF-FMP simulations of natural and anthropogenic components of the hydrologic cycle, the distribution and dynamics of supply and demand in the study area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.507-519
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• 2013

This study is to evaluate agricultural water supply capacity in Geum river basin (9,865 $km^2$), one of the 5 big river basin of South Korea using MODSIM-DSS (MODified SIMyld-Decision Support System) model. The model is a generalized river basin decision support system and network flow model developed at Colorado State University designed specifically to meet the growing demands and pressures on river basin management. The model was established by dividing the basin into 14 subbasins and the irrigation facilities viz. agricultural reservoirs, pumping stations, diversions, culverts and groundwater wells were grouped and networked within each subbasin and networked between subbasins including municipal and industrial water supplies. To prepare the inflows to agricultural reservoirs and multipurpose dams, the Soil and Water Assessment Tool (SWAT) was calibrated using 6 years (2005-2010) observed dam inflow and storage data. By MODSIM run for 8 years from 2004 to 2011, the agricultural water shortage had occurred during the drought years of 2006, 2008, and 2009. The agricultural water shortage could be calculated as 282 $10^6m^3$, 286 $10^6m^3$, and 329 $10^6m^3$ respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.1039-1047
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• 2020

Atmospheric rivers, which transport large amount of water vapor from mid-latitude to the inland, are an important driving force of water cycle and extreme hydrologic phenomenas. The main objective of this study is to analyze the hydrological impact of the AR landfalls on the Korean Peninsula in 2000 - 2015. The result showed that the AR is closely related to the characteristics of precipitation, water level and runoff in the Korean Peninsula. The landfalls of the AR affected about 57% of annual precipitation on the Korean Peninsula, and had a greatest impact on the summer rainfall. It also affected the water level and runoff at the five major rivers of Korea, and water levels exceeding the thresholds of flood warning were observed when the AR landed. Moreover, it was found that the runoff above the third quartile with AR landfalls. These results suggest that the AR not only has a significant influence on the hydrological characteristics of the Korean Peninsula, but also have a close relationship with the extreme hydrological events like floods. The results of this study are expected to be used as the reference for the analysis of the impact of the AR on the various fields in the Korean Peninsula.

• Journal of Korea Water Resources Association
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• v.32 no.5
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• pp.525-535
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• 1999

An optimal unit commitment model for efficient management of water and energy resources in a basin using combined mixed integer programming is developed. The combined mixed integer programming model is able to solve the inconsistency problem that may occur from mixed integer programming models. The technique which enables the use of conditional constraints and either-or constraints in the linear programming is also suggested. As a result of applying the combined mixed integer programming model to Lower Colorado River Basin in United States. the basin efficiency is decreased by 1.53% from the results of the mixed integer programming, while it is increased by 0.67% from the results of the historical operation. It is found that the decreased allowable error between power supplies and demands in the combined mixed integer programming causes the decreased basin efficiency.

• Wind and Structures
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• v.20 no.2
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• pp.249-274
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• 2015

Long-span cable-stayed bridges exhibit some features which are more critical than typical long span bridges such as geometric and aerodynamic nonlinearities, higher probability of the presence of multiple vehicles on the bridge, and more significant influence of wind loads acting on the ultra high pylon and super long cables. A three-dimensional nonlinear fully-coupled analytical model is developed in this study to improve the dynamic performance prediction of long cable-stayed bridges under combined traffic and wind loads. The modified spectral representation method is introduced to simulate the fluctuating wind field of all the components of the whole bridge simultaneously with high accuracy and efficiency. Then, the aerostatic and aerodynamic wind forces acting on the whole bridge including the bridge deck, pylon, cables and even piers are all derived. The cellular automation method is applied to simulate the stochastic traffic flow which can reflect the real traffic properties on the long span bridge such as lane changing, acceleration, or deceleration. The dynamic interaction between vehicles and the bridge depends on both the geometrical and mechanical relationships between the wheels of vehicles and the contact points on the bridge deck. Nonlinear properties such as geometric nonlinearity and aerodynamic nonlinearity are fully considered. The equations of motion of the coupled wind-traffic-bridge system are derived and solved with a nonlinear separate iteration method which can considerably improve the calculation efficiency. A long cable-stayed bridge, Sutong Bridge across the Yangze River in China, is selected as a numerical example to demonstrate the dynamic interaction of the coupled system. The influences of the whole bridge wind field as well as the geometric and aerodynamic nonlinearities on the responses of the wind-traffic-bridge system are discussed.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.85-85
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• 2020

South Korea experiences few large scale erosion and sedimentation problems, however, there are numerous local sedimentation problems. A reliable and consistent approach to modelling and management for sediment processes are desirable in the country. In this study, field measurements of sediment concentration from 34 alluvial river basins in South Korea were used with the Modified Einstein Procedure (MEP) to determine the total sediment load at the sampling locations. And then the Flow Duration-Sediment Rating Curve (FD-SRC) method was used to estimate the specific degradation for all gauging stations. The specific degradation of most rivers were found to be typically 50-300 tons/㎢·yr. A model tree data mining technique was applied to develop a model for the specific degradation based on various watershed characteristics of each watershed from GIS analysis. The meaningful parameters are: 1) elevation at the middle relative area of the hypsometric curve [m], 2) percentage of wetland and water [%], 3) percentage of urbanized area [%], and 4) Main stream length [km]. The Root Mean Square Error (RMSE) of existing models is in excess of 1,250 tons/㎢·yr and the RMSE of the proposed model with 6 additional validations decreased to 65 tons/㎢·yr. Erosion loss maps from the Revised Universal Soil Loss Equation (RUSLE), satellite images, and aerial photographs were used to delineate the geospatial features affecting erosion and sedimentation. The results of the geospatial analysis clearly shows that the high risk erosion area (hill slopes and construction sites at urbanized area) and sedimentation features (wetlands and agricultural reservoirs). The result of physiographical analysis also indicates that the watershed morphometric characteristic well explain the sediment transport. Sustainable management with the data mining methodologies and geospatial analysis could be helpful to solve various erosion and sedimentation problems under different conditions.