• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.504-505
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• 2003

국지규모의 기상해석을 위한 기존의 방법은 중규모 모델로부터 down-nesting에 의한 방법으로 모델의 해상도를 향상시켜 기상장을 해석하는 것이 일반적이다(Clark and Farley, 1984). 그러나, 이 방법은 단지 물리적인 격자구조만을 향상시킨 것으로 근본적인 모델 내부 구조의 향상을 가져온 것은 아니다. 따라서, 다양한 토지 피복이 혼재해 있는 도심지를 대상으로 하는 고해상도의 국지기상 모델에 대하여 계산시간과 정확성을 고려한 효율적인 지표면 열수지 모델의 구축이 요구되고 있다. (중략)

• Korean Journal of Hydrosciences
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• v.2
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• pp.69-84
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• 1991

US NWS/NETWORK is applied for the analysis of the flood of July 11-15, 1981 through the Goan-Indogyo reach of the Han River. For the flood hydrography synthesis of the lateral inflows from the major tributaries into the main reach the Cleak method is employed. NETWORK coupled with the Clark method of hydrography synthesis simulated with a fair accuracy the oberved flood hydrograph at the downstream boundary of the routing reach. The dffect of SCS runoff curve number for fributary flood synthesis is evaluated. The characteristics of the station variations and time variations of the flood discharges in the reach is also analyzed.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.505-516
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• 2012

The study applies a hydrologic simulation model, HEC-1 developed by Hydrologic Engineering Center to Daecheong dam watershed for modeling hourly inflows of Daecheong dam. Although the HEC-1 model provides an automatic optimization technique for some of the parameters, the built-in optimization model is not sufficient in estimating reliable parameters. In particular, the optimization model often fails to estimate the parameters when a large number of parameters exist. In this regard, a main objective of this study is to develop Bayesian Markov Chain Monte Carlo simulation based HEC-1 model (BHEC-1). The Clark IUH method for transformation of precipitation excess to runoff and the soil conservation service runoff curve method for abstractions were used in Bayesian Monte Carlo simulation. Simulations of runoff at the Daecheong station in the HEC-1 model under Bayesian optimization scheme allow the posterior probability distributions of the hydrograph thus providing uncertainties in rainfall-runoff process. The proposed model showed a powerful performance in terms of estimating model parameters and deriving full uncertainties so that the model can be applied to various hydrologic problems such as frequency curve derivation, dam risk analysis and climate change study.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.3
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• pp.17-27
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• 2008

In this study, three different unit hydrograph methods (Snyder, SCS, Clark) in the HEC-HMS were compared to find better fit with the observed data in the small agricultural watershed. Baran watershed, having $3.85km^2$ in size, was selected as a study watershed. The watershed input data for HEC-HMS were retrieved using HEC-GeoHMS which was developed to assist making GIS input data for HEC-HMS. Rainfall and water flow data were monitored since 1996 for the study watershed. Fifty five storms from 1996 to 2003 were selected for model calibration and verification. Three unit hydrograph methods were compared with the observed data in terms of simulated peak runoff, peak time and total direct runoff for the selected storms. The results showed that the coefficient of determination ($R^2$) for the observed peak runoff was $0.8666{\sim}0.8736$ and root mean square error, RMSE, was $5.25{\sim}6.37\;m^3/s$ for calibration stages. In the model verification, $R^2$ for the observed peak runoff was $0.8588{\sim}0.8638$ and RMSE was $9.57{\sim}11.80\;m^3/s$, which were slightly less accurate than the calibrated data. The simulated flood hydrographs were well agreed with the observed data. SCS unit hydrograph method showed best fit, but there was no significant difference among the three unit hydrograph methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.17-27
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• 2020

The objective of this study was to assess the flood probability based on temporal distribution of forecasted-rainfall in Cheongmicheon watershed. In this study, 6-hr rainfalls were disaggregated into hourly rainfall using the Multiplicative Random Cascade (MRC) model, which is a stochastic rainfall time disaggregation model and it was repeated 100 times to make 100 rainfalls for each storm event. The watershed runoff was estimated using the Clark unit hydrograph method with disaggregated rainfall and watershed characteristics. Using the peak discharges of the simulated hydrographs, the probability distribution was determined and parameters were estimated. Using the parameters, the probability density function is shown and the flood probability is calculated by comparing with the design flood of Cheongmicheon watershed. The flood probability results differed for various values of rainfall and rainfall duration. In addition, the flood probability calculated in this study was compared with the actual flood damage in Cheongmicheon watershed (R² = 0.7). Further, this study results could be used for flood forecasting.

• Journal of the Korean Geographical Society
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• v.38
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• pp.61-74
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• 1988

Changing patterns of population densities in urban centers are different between Western countries and non-Western countries. Although Seoul is located in a non-Western country, the result of this study shows that its pattern of population density falls into the category of Western cities. Through the examination of three population density gradient models, it is clear that no model can precisely explain the population distribution of Seoul over time. Some of the models partly indicate the actual population distrisbution. The Clark model is appropriate to denote population distribution in the center of Seoul at an early stage in development. The Sherratt model cannot adequately explain the population distribution of Seoul.

• Journal of Environmental Science International
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• v.12 no.9
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• pp.949-955
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• 2003

The subject basin of the research was the basin of Yeongcheon Dam located in the upper reaches of the Kumho River. The parameters of the model were derived from the results of abstracting topological properties out of rainfall-runoff observation data about heavy rains and Digital Elevation Modeling(DEM) materials. This research aimed at suggesting the applicability of the CELLMOD Model, a distribution-type model, in interpreting runoff based on the topological properties of a river basin, by carrying out runoff interpretation far heavy rains using the model. To examine the applicability of the model, the calculated peaking characteristics in the hydrograph was analyzed in comparison with observed values and interpretation results by the Clark Model. According to the result of analysis using the CELLMOD Model proposed in the present research for interpreting the rainfall-runoff process, the model reduced the physical uncertainty in the rainfall-runoff process, and consequently, generated improved results in forecasting river runoff. Therefore it was concluded that the algorithm is appropriate for interpreting rainfall-runoff in river basins. However, to enhance accuracy in interpreting rainfall-runoff it is necessary to supplement heavy rain patterns in subject basins and to subdivide a basin into minor basins for analysis. In addition, it is necessary to apply the model to basins that have sufficient observation data, and to identify the correlation between model parameters and the basin characteristics(channel characteristics).

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.52-67
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• 2008

Recently, frequent occurrence of flash floods caused by climactic change has necessitated prompt and quantitative prediction of precipitation. In particular, the usability of rainfall radar that can carry out real-time observation and prediction of precipitation behavior has increased. Moreover, the use of distributed hydrological model that enables grid level analysis has increased for an efficient use of rainfall radar that provides grid data at 1km resolution. The use of distributed hydrologic model necessitates grid-type spatial data about target basins; to enhance reliability of flood runoff simulation, the use of visible and precise data is necessary. In this paper, physically based $Vflo^{TM}$ model and ModClark, a quasi-distributed hydrological model, were used to carry out flood runoff simulation and comparison of simulation results with data from Imjin River Basin, two-third of which is ungauged. The spatial scope of this study was divided into the whole Imjin River basin area, which includes ungauged area, and Imjin River basin area in South Korea for which relatively accurate and visible data are available. Peak flow and lag time outputs from the two simulations of each region were compared to analyze the impact of uncertainty in topographical parameters and soil parameters on flood runoff simulation and to propose effective methods for flood runoff simulation in ungauged regions.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1262-1266
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• 2004

The current combining of computer and geographic information technology. The result of such research oil determinate objective factors of hydrologic-topographical parameters through joining hydrology and GIS(Geographic Information System). In this study, we wish to offer the base data to determinate hydrologic-topographical parameters request of runoff model analysis in this basin. First, we computed the CN(curve number) by using GIS, and then classify the digital map of soil group and landuse on the Sulma river basin. Second, we used Avenue Script to calculate the height of efficient GIS work before using the Clark model to work out flood runoff flow.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.