• Current Research on Agriculture and Life Sciences
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• v.32 no.3
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• pp.135-143
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• 2014

The variability and temporal trends of the annual and seasonal minimum and maximum temperature, rainfall, relative humidity, wind speed, sunshine hours, and runoff were analyzed for 5 major rivers in Korea from 1960 to 2010. A simple regression and non-parametric methods (Mann-Kendall test and Sen's estimator) were used in this study. The analysis results show that the minimum temperature ($T_{min}$) had a higher increasing trend than the maximum temperature ($T_{max}$), and the average temperature increased by about $0.03^{\circ}C\;yr.^{-1}$. The relative humidity and wind speed decreased by $0.02%\;yr^{-1}$ and $0.01m\;s^{-1}yr^{-1}$, respectively. With the exception of the Han River basin, the regression analysis and Mann-Kendall and Sen results failed to detect trends for the runoff and rainfall over the study period. Rapid land use changes were linked to the increase in the runoff in the Han River basin. The sensitivity of the evapotranspiration and ultimately the runoff to the meteorological variables was in the order of relative humidity > sunshine duration > wind speed > $T_{max}$ > $T_{min}$. Future studies should investigate the interaction of the variables analyzed herein, and their relative contributions to the runoff trends.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.851-860
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• 2012

In this study, to evaluate the abatement of runoff pollution loads by the road sweeping(cleaning), various investigations are implemented at the sample area of the highway. As the results of evaluating the removal efficiency of pollutants along road cleaning, TSS showed about 78 % of the removal efficiency and COD showed 49 % of removal efficiency through the operation of cleaning vehicle of vacuum suction method. In case of TN and TP, they showed the relatively-lower removal efficiency by 30~35 %. TSS removal efficiency along the number of cleaning appeared about 60 % in case of one time of cleaning and the additional removal effect did not appear though the number of cleaning increased to two times. With running speed of cleaning vehicle, TSS removal ratio is lessened from 60 % to 20 % when cleaning vehicle speed up to 20 km/hr from 6 km/hr. It seems that the reasons why the removal efficiencies are inversely proportional to its speed are related to the lower vacuum efficiencies and the disturbed particles on the road. In the pollutant build-up analysis, it is showed that it takes more time to the critical pollutant build-up in the shoulder than the center of the road. It is also showed that the proper cleaning cycle is recommended as 4~6 dry weather days without rainfall events.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.65-71
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• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.15-25
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• 2006

Using kinematic wave equation, the influence of moving storms to runoff was analysised with a focus on watersheds. Watershed shapes used are the oblong, square and elongated shape, and the distribution types of moving storms used are uniform, advanced and intermediate type. The runoff hydrographs according to the rainfall distribution types were simulated and the characteristics were explored for the storms moving down, up and cross the watershed with various velocity. The shape, peak time and peak runoff of a runoff hydrograph are significantly influenced by spatial and temporal variability in rainfall and watershed shapes. A rain storm moving in the cross direction of channel flow produces a higher peak runoff than in the downstream direction and upstream direction. A peak runoff from a storm moving downstream exceeds that from a storm moving upstream. For storms moving downstream peak time was more delayed than for other storm direction in the case of elongated watershed. The runoff volume and time base of the hydrograph decreased with the increasing storm speed.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.121-129
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• 2018

Rainfall falling in the impervious area of the cities flows over the surface and into the stormwater pipe networks to be discharged from the catchment. Therefore, it is very important to determine the size of stormwater pipes based on the peak discharge to mitigate urban flood. Climate change causes the severe rainfall in the small area, then the peak rainfall can not be discharged due to the capacity of the stormwater pipes and causes the urban flood for the short time periods. To mitigate these type of flood, the large stormwater pipes have to be constructed. However, the economic factor is also very important to design the stormwater pipe networks. In this study, 4 urban catchments were selected from the frequently flooded cities. Rainfall data from Seoul and Busan weather stations were applied to calculate runoff from the catchments using SWMM model. The characteristics of the peak runoff were analyzed using linear regression model and the 95% confidence interval and the coefficient of variation was calculated. The drainage density was calculated and the runoff characteristics were analyzed. As a result, the drainage density were depended on the structure of stormwater pipe network whether the structures are dendritic or looped. As the drainage density become higher, the runoff could be predicted more accurately. it is because the possibility of flooding caused by the capacity of stormwater pipes is decreased when the drainage density is high. It would be very efficient if the structure of stormwater pipe network is considered when the network is designed.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.3
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• pp.4184-4194
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• 1976

Located in the southwestern part of Korea, the Yong San Gang river flows generally northeast to southwest, and because of the specific location, topography and climate, the basin area is subject to recurrent drought and flood damages. To eliminate the cause of such damages and ensure an increase in the farm income by means of effective irrigation supply and increased cropping intensity, efforts are being made to speed up implementation of an integrated agricultural development project which would include construction. of an estuary dam and irrigation facilities as well as land development and tidal reclarnation. In formulating a basin development project plan, it is necessary to study a series of long-term runoff data. The catchment area at the proposed estuary damsite is 3,471$\textrm{km}^2$ with the total length of the river channel up to this point reaching 138km. An analysis of runoff in this area was carried out. Rainfall was estimated by the Thiessen Network based on records available from 15 of the rainfall observation stations within the area. Out of the 15 stations, Kwang Ju and Mok Po stations were keeping long-term precipitation records exceeding some 60 years while the others were in possession of only 5-10 years records. The long-term records kept by those stations located in the center of the basin were used as base records and records kept by the remaining stations were supplemented using the coefficient of correlation between the records kept by the base stations and the remainder. The analyses indicate that the average annual rainfall measured at Kwang Ju during 1940-1972 (33 years) amounts to 1,262mm and the areal rainfall amounts to 1,236mm. For the purpose of runoff analysis, 7 observatories, were set up in the middle and lower reaches of the river and periodic measurements made by these stations permitted analysis of water levels and river flows. In particular, the long-term data available from Na Ju station significantly contributed to the analysis. The analysis, made by 4-stage Tank method, shows that the average annual runoff during 1940-1972 amounts to 2,189 million ㎥ at the runoff rate of 51%. As for the amount of monthly runoff, the maximum is 484.2 million ㎥ in July while the minimum is 48.3 million ㎥ in January.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.887-894
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• 2019

We proposed a GPU (Grapic Processing Unit) accelerated kinematic wave model for rainfall runoff simulation and tested the accuracy and speed up performance of the proposed model. The governing equations are the kinematic wave equation for surface flow and the Green-Ampt model for infiltration. The kinematic wave equations were discretized using a finite volume method and CUDA fortran was used to implement the rainfall runoff model. Several numerical tests were conducted. The computed results of the GPU accelerated kinematic wave model were compared with several measured and other numerical results and reasonable agreements were observed from the comparisons. The speed up performance of the GPU accelerated model increased as the number of grids increased, achieving a maximum speed up of approximately 450 times compared to a CPU (Central Processing Unit) version, at least for the tested computing resources.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.107-113
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• 2019

The flood forecasting model currently used in Korea calculates the runoff of basin using the lumped rainfall-runoff model and estimates the river level using the river and reservoir routing models. The lumped model assumes homogeneous drainage zones in the basin. Therefore, it can not consider various spatial characteristics in the basin. In addition, the rainfall data used in lumped model also has the same limitation because of using the point scale rainfall data. To overcome the limitations as mentioned above, many researchers have studied to apply the distributed rainfall-runoff model to flood forecasting system. In this study, to apply the Grid-based Rainfall-Runoff Model (GRM) to the Korean flood forecasting system, the optimal resolution is determined by analyzing the difference of the results of the runoff according to the various resolutions. If the grid size is to small, the computation time becomes excessive and it is not suitable for applying to the flood forecasting model. Even if the grid size is too large, it does not fit the purpose of analyzing the spatial distribution by applying the distributed model. As a result of this study, the optimal resolution which satisfies the accuracy of the bsin runoff prediction and the calculation speed suitable for the flood forecasting was proposed. The accuracy of the runoff prediction was analyzed by comparing the Nash-Sutcliffe model efficiency coefficient (NSE). The optimal resolution estimated from this study will be used as basic data for applying the distributed rainfall-runoff model to the flood forecasting system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• 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.