• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.701-705
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• 2022

Reliable drought inflow scenarios are required to plan reservoirs in response to the present severe drought-like conditions. However, the previously developed method for generating drought inflows, the K-water cumulative difference method (KCM), is considered inadequate owing to its potential for negative inflow, reversal phenomena, and overestimation. Nevertheless, the occurrence of these aspects has not been theoretically analyzed. Hence, this study employed the quantile function and frequency factor for log-normal and Gumbel distributions to quantify the contributing factors of these limitations. Consequently, it was found that the negative inflows are generated when the difference in the location parameters, during the accumulation process, exceeds that of the scale parameters. In addition, as the standard deviation decrease during the accumulation process, the reversal phenomena, and inflated values prevailed.

• Journal of the Korean Geotechnical Society
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• v.30 no.6
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• pp.23-39
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• 2014

An artificial rainwater reservoir installed in urban areas for recycling rainwater is an eco-friendly facility for reducing storm water effluence. However, in order to recycle the rainwater directly, the artificial rainwater reservoir requires an auxiliary system that can remove non-point source pollutants included in the initial rainfall of urban area. Therefore, the conventional soil filtration technology is adopted to capture non-point source pollutants in an economical and efficient way in the purification system of artificial rainwater reservoirs. In order to satisfy such a demand, clogging characteristics of the sand filter layers with different grain-size distributions were studied with real non-point source pollutants. For this, a series of lab-scale chamber tests were conducted to make a prediction model for removal of non-point source pollutants, based on the clogging theory. The laboratory chamber experiments were carried out by permeating two types of artificially contaminated water through five different types of sand filter layers with different grain-size distributions. The two artificial contaminated waters were made by fine marine-clay particles and real non-point source pollutants collected from motorcar roads of Seoul, Korea. In the laboratory chamber experiments, the concentrations of the artificial contaminated water were measured in terms of TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) and compared with each other to evaluate the performance of sand filter layers. In addition, the accumulated weight of pollutant particles clogged in the sand filter layers was estimated. This paper suggests a prediction model for removal of non-point source pollutants with theoretical consideration of the physical characteristics such as the grain-size distribution and composition, and change in the hydraulic conductivity and porosity of sand filter layers. The lumped parameter ${\theta}$ related with the clogging property was estimated by comparing the accumulated weight of pollutant particles obtained from the laboratory chamber experiments and calculated from the prediction model based on the clogging theory. It is found that the lumped parameter ${\theta}$ has a significant influence on the amount of the pollutant particles clogged in the pores of sand filter layers. In conclusion, according to the clogging prediction model, a double-sand-filter layer consisting of two separate layers: the upper sand-filter layer with the effective particle size of 1.49 mm and the lower sand-filter layer with the effective particle size of 0.93 mm, is proposed as the optimum system for removing non-point source pollutants in the field-sized artificial rainwater reservoir.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.303-313
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• 2017

In environmental impact assessments for large urban development projects, the Korean government requires analysis of stormwater runoff before, during and after the projects. Though hydrological models are widely used to analyze and prepare for surface runoff during storm events, accuracy of the predicted results have been in question due to limited amount of field data for model calibrations. Intensive field measurements have been made for storm events between July 2015 and July 2016 at a sub-basin of the Gwanpyung-cheon, Daejeon, Republic of Korea using an automatic monitoring system and also additional manual measurements. Continuous precipitation and surface runoff data used for utilization of SWMM model to predict surface runoff during storm events with improved accuracy. The optimal values for Manning's roughness coefficient and values for depression storage were estimated for pervious and impervious surfaces using three representative infiltration methods; the Curve Number Methods, the Horton's Method and the Green-Ampt Methods. The results of the research is expected to be used more efficiently for urban development projects in Korea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.321-331
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• 2008

A model has been constructed in this study for the investigation of physical characteristics of the Gyeonggi Bay and Han River estuary. MIKE 21 HD (HyDrodynamics) has been used for the uncertainty analysis of the tide of the Gyeonngi Bay and Han River estuary. A total of 15 model experiments have been performed for the hydrodynamic parts and the analysis of results have been made in terms of RMSD (Root-mean square deviation) which has been frequently employed in the suitability analysis of hydrological data since the introduction by NERC(1975), U.K. A smaller value of RMSD indicates the more suitability of a parameter to the model. Analysis of the hydrodynamic results has shown that RMSD of the mean tidal range has the largest value of 0.1148 at Yeomha channel while has the smallest value of 0.0400 at Yeonphyong-do, indicating that the uncertainty in the mean tidal range on near-shore side is larger than that of offshore side. Experiment with reduced water depth by 10% has produced a most significant increase in RMSD. It is therefore implied that the model response changes more sensitively to water depth rather than grid sizes, open boundary forcing and river discharge.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.7 no.1
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• pp.39-47
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• 2009

The METRO-K is a model for a radiological dose assessment due to a radioactive contamination in the Korean urban environment. The model has been taken part in the Urban Remediation Working Group within the IAEA's (International Atomic Energy Agency) EMRAS (${\mathbf{\underline{E}}}nvironmental$ ${\mathbf{\underline{M}}}odeling$ for ${\mathbf{\underline{RA}}}diation$ ${\mathbf{\underline{S}}}afety$) program. The Working Croup designed for the intercomparison of radioactive contamination to be resulted from the explosion of a radiological dispersal device in a hypothetical city. This paper dealt intensively with a part among a lot of predictive results which had been performed in the EMRAS program. The predictive results of three different models (METRO-K, RESRAD-RDD, CPHR) were submitted to the Working Group. The gap of predictive results was due to the difference of mathemathical modeling approaches, parameter values, understanding of assessors. Even if final results (for example, dose rates from contamintaed surfaces which might affect to a receptor) are similar, the understanding on the contribution of contaminated surfaces showed a great difference. Judging from the authors, it is due to the lack of understanding and information on radioactive terrors as well as the social and cultural gaps which assessors have been experienced. Therefore, it can be known that the experience of assessors and their subjective judgements might be important factors to get reliable results. If the acquisition of a little additional information is possible, it was identified that the METRO-K might be a useful tool for decision support against contamination resulting from radioactive terrors by improving the existing model.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.16-24
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• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.68-76
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• 2014

The accurate estimation of construction cost is important to a successful development in construction projects. In previous studies, the construction cost are estimated by statistical methods. Among the statistical methods, support vector regression (SVR) has attracted a lot of attentions because of the generalization ability in the field of cost estimation. However, despite the simplicity of the parameter to be adjusted, it is not easy to find optimal parameters. Therefore, to build an effective SVR model, SVR's parameters must be set properly without additional data handling loads. So this study proposes a novel approach, known as genetic algorithm (GA), which searches SVR's optimal parameters, then adopt the parameters to the SVR model for estimating cost in the early stage of apartment housing projects. The aim of this study is to propose a GA-SVR model and examine the feasibility in cost estimation by comparing with multiple regression analysis (MRA). The experimental results demonstrate the estimating performance based on the percentage of estimations within 25% and find it can effectively do the accurate estimation without through the trial and error process.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.395-405
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• 2021

When issuing forecasts and alerts for agricultural drought, the relevant ministries only rely on the observation data from the reservoirs managed by the Korea Rural Community Corporation, which creates gaps between the drought analysis results at the local (si/gun) governments and the droughts actually experienced by local residents. Closing these gaps requires detailed local geoinformation on reservoirs, which in turn requires the information on reservoirs managed by local governments across Korea. However, installing water level and flow measurement equipment at all of the reservoirs would not be reasonable in terms of operation and cost effectiveness, and an alternate approach is required to efficiently generate information. In light of the above, this study validates and calibrates the parameters of the TANK model for reservoir basins, divided them into groups based on the characteristics of different basins, and applies the grouped parameters to unmeasured local government reservoirs to estimate and assess inflow. The findings show that the average determinant coefficient and the NSE of the group using rice paddies and inclinations are 0.63 and 0.62, respectively, indicating better results compared with the basin area and effective storage factors (determinant coefficient: 0.49, NSE: 0.47). The findings indicate the possibility of utilizing the information regarding unmeasured reservoirs managed by local governments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.515-524
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• 2022

The turnout system of the sleeper floating tracks (STEDEF) on urban transit is a Anti-vibration track composed of a wooden sleeper embedded in a concrete bed and a sleeper resilience pad under the sleeper. Therefore, deterioration and changes in spring stiffness of the sleeper resilience pad could be cause changes in sleeper support conditions. The damage amount of manganese crossings that occurred during the current service period of about 21 years was investigated to be about 17% of the total amount of crossings, and it was analyzed that the damage amount increased after 15 years of use (accumulated passing tonnage of about 550 million tons). In this study, parameter analysis (wheel position, sleeper support condition, and dynamic wheel load) was performed using a three-dimensional numerical model that simulated real manganese crossing and wheel profile, to analyze the damage type and cause of manganese crossing that occurred in the actual field. As a result of this study, when the voided sleeper occurred in the sleeper around the nose, the stress generated in the crossing nose exceeded the yield strength according to the dynamic wheel load considering the design track impact factor. In addition, the analysis results were evaluated to be in good agreement with the location of damage that occurred in the actual field. Therefore, in order to minimize the damage of the manganese crossing, it is necessary to keep the sleeper support condition around the nose part constant. In addition, by considering the uniformity of the boundary conditions under the sleepers, it was analyzed that it would be advantageous to to replace the sleeper resilience pad together when replacing the damaged manganese crossing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.