• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Journal of Wetlands Research
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• v.11 no.3
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• pp.81-88
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• 2009

The cows manure has been used as fertilizers in farmland because of enough nutrients. However, excess nutrients can be washed off during a storm and affected on nearby waterbodies. In this paper, the runoff characteristics from farmland were studied to determine the watershed runoff rate. As results of estimating, watershed runoff rates with short-term runoff are BOD 0.6%, CODcr 0.3 %, CODMn 0.1 %, T-N 0.8 % and T-P 1.0 % On the other hand, they with long-term runoff are BOD 3.6 %, CODcr 1.0 %, CODMn 0.9 %, T-N 4.9 % and T-P 4.8 %. It is clean that the watershed runoff rates increase according to the rainfall runoff time.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• Water for future
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• v.24 no.3
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• pp.95-104
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• 1991

The study was focused on developing a new model to estimate annual runoff. This model can be used to estimate the available water resources for ungaged catchments for long-term water resources development planning. Data used in the model development were daily rainfall and daily runoff of the sample basin with record length from 1945 to 1988 years in Korea. The sample basin selected by consideration whether the flow is virgin and quality of discharge data is good. As a result, 46 stage gaging station were selected. Annual runoff was determined by sum of daily runoff calculated by daily stage data of the sample basin. Also, the annual mean precipitation by using daily rainfall data was estimated and the annual runoff ratio for each sample basin was calculated, and the annual mean runoff ratio was estimated. The linear regression model was proposed and calibrated using auunal mean precipitation values and geomorphological characteristics of the basins. To verify reasonableness of this model, the regression model was applied to the gaging stations which have historical data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Membrane and Water Treatment
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• v.10 no.1
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• pp.19-28
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• 2019

To minimize the impact of urbanization, accurate performance evaluation of Low Impact Development (LID) facilities is needed. In Korea, the method designed to evaluate large-scale non-point pollution reduction facilities is being applied to LID facilities. However, it has been pointed out that this method is not suitable for evaluating the performance of relatively small-scale installed LID facilities. In this study, a new design formula was proposed based on the ratio of LID facility area and contributing drainage area, for estimating the Stormwater Interception Ratio (SIR) for LID facilities. The SIR was estimated for bio-retentions, infiltration trenches and vegetative swales, which are typical LID facilities, under various conditions through long-term stormwater simulation using the LID module of EPA SWMM. Based on the results of these numerical experiments, the new SIR formula for each LID facility was derived. The sensitivity of the proposed SIR formula to local rainfall properties and design variables is analysed. In addition, the SIR formula was compared with the existing design formula, the Rainfall Interception Ratio (RIR).

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.61-61
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• 2020

최근 수문기상학 분야에서 레이더 강수량을 활용한 응용연구가 활발하게 진행되고 있다. 하지만 레이더 강수량은 대류성 및 층상형 등과 같이 강수특성을 기준으로 레이더 반사도-강수량(Reflectivity-Rainfall, Z-R) 관계식 매개변수를 시공간적으로 동일하게 적용하여 레이더 강수량을 산정하는 방법론은 지상관측 강수량과 정량적인 편의 오차(systematic error)를 발생시킬 수 있는 문제점이 있다. 본 연구는 장기간의 레이더 합성장 반사도를 활용하여 Z-R 관계식 매개변수를 산정하였으며, 이 과정에서 Bayesian 추론 기법을 도입하여 Z-R 관계식 매개변수의 불확실성을 정량화하였다. 추가적으로 편의 오차를 최소화하기 위하여 계절성을 고려한 Z-R 관계식을 산정하였다. 건기와 우기로 구분하여 산정된 Z-R 관계식 매개변수의 공간적으로 변동성과 더불어 강수의 계절적 특성에 기인하는 Z-R 관계식 매개변수의 역비례 관계를 확인하였다. 최종적으로, 제안된 방법론으로 산정된 레이더 강수장은 일반적으로 레이더 강수량 산정에 널리 이용되는 Marshall-Palmer Z-R 관계식으로 산정된 강수장에 비하여 우수한 통계지표를 제시하였다.

• Atmosphere
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• v.34 no.2
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• pp.107-121
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• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.

• Water Engineering Research
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• v.2 no.4
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• pp.219-229
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• 2001

Synthetic unit hydrograph equations for rainfall run-off characteristics analysis and estimation of design flood have long and quite frequently been presented, the Snyder and SCS synthetic unit hydrograph. The major inputs to the Snyder and SCS synthetic unit hydrograph are lag time and peak coefficient. In this study, the methods for estimating lag time and peak coefficient for small watersheds proposed by Zhao and McEnroe(1999) were applied to the Kum river basin in Korea. We investigated lag times of relatively small watersheds in the Kum river basin in Korea. For this investigation the recent rainfall and stream flow data for 10 relatively small watersheds with drainage areas ranging from 134 to 902 square kilometers were gathered and used. 250 flood flow events were identified along the way, and the lag time for the flood events was determined by using the rainfall and stream flow data. Lag time is closely related with the basin characteristics of a given drainage area such as channel length, channel slope, and drainage area. A regression analysis was conducted to relate lag time to the watershed characteristics. The resulting regression model is as shown below: ※ see full text (equations) In the model, Tlag is the lag time in hours, Lc is the length of the main river in kilometers and Se is the equivalent channel slope of the main channel. The coefficient of determinations (r$^2$)expressed in the regression equation is 0.846. The peak coefficient is not correlated significantly with any of the watershed characteristics. We recommend a peak coefficient of 0.60 as input to the Snyder unit-hydrograph model for the ungauged Kum river watersheds

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.458-463
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• 2009

The purpose of this study was to evaluate the changes of surface runoff by comparisons between burned and unburned area after forest fire. The amount of surface runoff in burned area was more high 1.72 times in the year of fire, 1.44 times in one year later, 1.38 times in five years later and 1.16 times in ten years later than those of unburned area. Therefore, surface runoff in the burned area almost tended to be stabilized like unburned area ten year later after forest fire. The most affecting factors on the amount of surface runoff in burned and unburned area were number of unit rainfall, number of rainfall accumulated and unit rainfall. But coverage was shown to mitigate the amount of surface runoff in burned and unburned area.