• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.3
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• pp.23-35
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• 1993

The objectives of this paper were to validate the proposed network flow model using field data and to demonstrate the model applicability for various purposes. The model was tested with data from the Banweol watershed, where an intentive streamflow gauging system has been established. Model parameters were not calibrated with field data so that it can be validated as ungaged conditions. Three different schemes were employed to represent the drainage system of the tested watershed : a single, complex, and detailed network. The single network assumed the watershed as a cell, while complex and detailed networks considered several cells. The results from different schemes were individually compared satisfactorily to the observed daily stages at the Banweol reservoir located at the outlet of the watershed. The results from three schemes were in close agreement with each other, Justifying that the model performs very well for different network schemes being used. Daily streamflow from three network schemes was compared for a selected reach within the watershed. The results were very close to each other regardless of network formulation. And the model was applied to simulate daily streamflow before and after the construction of a reservoir at a reach. The differences were discussed, which reflected the influences of the dam construction upon the downstream hydrology. Similar appliocations may be possible to identify the effects of hydraulic structures on streamflow.

• Journal of Environmental Science International
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• v.13 no.7
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• pp.611-618
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• 2004

The Neural Network Models which mathematically interpret human thought processes were applied to resolve the uncertainty of model parameters and to increase the model's output for the streamflow forecast model. In order to test and verify the flood discharge forecast model eight flood events observed at Kumho station located on the midstream of Kumho river were chosen. Six events of them were used as test data and two events for verification. In order to make an analysis the Levengerg-Marquart method was used to estimate the best parameter for the Neural Network model. The structure of the model was composed of five types of models by varying the number of hidden layers and the number of nodes of hidden layers. Moreover, a logarithmic-sigmoid varying function was used in first and second hidden layers, and a linear function was used for the output. As a result of applying Neural Networks models for the five models, the N10-6model was considered suitable when there is one hidden layer, and the Nl0-9-5model when there are two hidden layers. In addition, when all the Neural Network models were reviewed, the Nl0-9-5model, which has two hidden layers, gave the most preferable results in an actual hydro-event.

• Water for future
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• v.23 no.1
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• pp.109-118
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• 1990

In this study, the effectiveness of existing streamflow data collection networks in the Han and the Nakdong River Basin is evaluated for various gaging plans of 5, 10, 15 and 20years planning horizons by the nonlinear integer programming method, and also a technique for adjustment and planning of the existing network is provided for the purpose of increasing the efficiency of the network in terms of ecomony. The objective function is minimization of the average sampling mean square error of regional regression model with regression parameters estimated by generalized least squares method.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.1
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• pp.40-49
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• 1993

The Objectives of this paper were to develop a modified tank model that is capable of simulating daily streamflow from a small watershed using daily watershed evapotranspiration and to test the applicability of the model to different watersheds. Tank model was restructured to consist of three series of tanks, each of which may mathematically reflect watershed runoff mechanisms from different components of surface runoff, interflow, and baseflow. And pan evaporation was correlated to potential evapotranspiration estimated from a combination method, and was multiplied by monthly crop and landuse coefficients, and watershed storage coefficient to estimate the watershed evapotranspiration losses. Ten watersheds were selected to calibrate model parameters that were defined using an optimization scheme, and the results were correlated with watershed parameters. Simulated daily runoff was compared to the observed ones from the tested watersheds. The simulating results were in good agreement with the observed values when optimal and calibrated parameters were used. Ungaged conditions were also applied to compare simulated values to the observed. And the results were in fair conditions for all the tested watersheds which differ considerably in their sizes, landuse types, and physiological features.

• 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 Korean Society on Water Environment
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• v.26 no.5
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• pp.860-870
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• 2010

Long term monthly monitoring data showed that the water quality of streams flowing into Lake Paldang has been improved by various strategy for water. However, the effect of quality on Lake Paldang is still insufficient because of nonpoint source from watershed. In order to evaluate quantifying methods for pollution source and make a suggestion on improvements, Storm Water Management Model (SWMM) was constructed by using data set from the water quality and streamflow monitoring network in the Kyoungan watershed for Total Maximum Daily Loads (TMDLs). Load duration curve (LDC) based on the result of the Kyoungan watershed SWMM indicated that the water quality criterion on $BOD_5$ was often exceeded in up-stream than down-stream. From flowrate-load correlation curve, SS load significantly increased as streamflow increases. 75.3% of streamflow and 62.1% of $BOD_5$ loads is discharged especially in the zone of high flows, but monitoring data set didn't provide proper information about the conditions and the patterns associated with storm events. Therefore, it is necessary to acquire representative data set for comparing hydrograph and pollutograph through monitoring experimental watershed and to establish methods for quantifying point and nonpoint source pollutant loads.

• Asian Journal of Innovation and Policy
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• v.10 no.2
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• pp.212-235
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• 2021

In spite of the increasing popularity of the Ricardian model for the study of the impact of climate change on agriculture, there has been few attempts to examine the role of interregional spillovers in this framework and all of them rely on geographical proximity-based weighting schemes. We remedy to this gap by focusing on the spatial externalities of surface water flow used for irrigation purposes and demonstrate that farmland value, the usual dependent variable used in the Ricardian framework, is a function of the climate variables experienced locally and in the upstream locations. This novel approach is tested empirically on a spatial panel model estimated across the counties of the Southwest USA over 1997-2012. This region is one of the driest in the country, hence its agriculture relies heavily on irrigated surface water. The results highlight how the weather conditions in upstream counties significantly affect downstream agriculture, thus the actual impact of climate change on agriculture and subsequent adaptation policies cannot overlook the streamflow network anymore.