• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.23-34
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• 2004

To validate the previous conceptual design of cover system, construction of the engineered barrier test facility is completed and the performance tests of the disposal cover system are conducted. The disposal test facility is composed of the multi-purpose working space, the six test cells and the disposal information space for the PR center. The dedicated detection system measures the water content, the temperature, the matric potential of each cover layer and the accumulated water volume of lateral drainage. Short-term experiments on the disposal cover layer using the artificial rainfall system are implemented. The sand drainage layer shows the satisfactory performance as intended in the design stage. The artificial rainfall does not affect the temperature of cover layers. It is investigated that high water infiltration of the artificial rainfall changes the matric potential in each cover layer. This facility is expected to increase the public information about the national radioactive waste disposal program and the effort for the safety of the planned disposal facility.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of Korean Society on Water Environment
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• v.25 no.5
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• pp.785-791
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• 2009

Using artificial rainfall simulator, the soil loss, which is deemed as the most cause of muddy water problem among Non-point source (NPS) pollutant, was studied by the analysis of direct runoff, groundwater discharge, and soil water storage properties concerned with rainfall intensity, slope of area, and land cover. The direct runoff showed increasing tendency in both straw covered and bared soil as slope increases from 5% to 20%. The direct runoff volume from straw covered surface were much lower than bared surface. The infiltration capacity of straw covered surface increased, because the surface sealing by fine material of soil surface didn't occur due to the straw covering. Under the same rainfall intensity and slope condition, 2.4~8.2 times of sediment yield were occurred from bared surface more than straw covered surface. The volume of infiltration increased due to straw cover and the direct runoff flow decreased with decrease of tractive force in surface. To understand the relationship of the rate of direct runoff, groundwater discharge, and soil water storage by the rainfall intensity, slope, and land cover, the statistical test was performed. It shows good relationship between most of factors, except between the rate of groundwater storage and rainfall intensity.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.509-512
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• 2003

Our country is serious difference of precipitation seasonally and about 66% of yearly mean rainfall is happening in concentration rainfall form between September on June. It requires consideration because of a lot of natural disasters by this downpour are produced. Slope failure is happened by artificial factor of creation of slope according to the land development, fill slope etc. and natural factor of rainfall, topography, nature of soil, soil quality, rock floor. Usually, Direct factor of failure slope is downpour. In this study, the Slope about among 55 places happened failure by downpour investigated occurrence position, geological etc and executed and inquire into character of rainfall connected with failure slope. Among character of rainfall, executed analysis about Max. hourly rainfall and cumulative rainfall of place that failure slope is situated and grasped the geological character of failure slope. Through this, inquire to character of failure slope by rainfall and take advantage of basis study for Hazard map creation.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.81-87
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• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.3
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• pp.34-44
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• 2020

In order to suggest performance analysis directions of ecological components based on a vegetation-based LID system model, this study seeks to analyze the statistical significance between monitoring results by using SWMM computer simulation and rainfall and run-off simulation devices and provide basic data required for a preliminary system design. Also, the study aims to comprehensively review a vegetation-based LID system's soil, a vegetation model, and analysis plans, which were less addressed in previous studies, and suggest a performance quantification direction that could act as a substitute device-type LID system. After monitoring artificial rainfall for 40 minutes, the test group zone and the control group zone recorded maximum rainfall intensity of 142.91mm/hr. (n=3, sd=0.34) and 142.24mm/hr. (n=3, sd=0.90), respectively. Compared to a hyetograph, low rainfall intensity was re-produced in 10-minute and 50-minute sections, and high rainfall intensity was confirmed in 20-minute, 30-minute, and 40-minute sections. As for rainwater run-off delay effects, run-off intensity in the test group zone was reduced by 79.8% as it recorded 0.46mm/min at the 50-minute point when the run-off intensity was highest in the control group zone. In the case of computer simulation, run-off intensity in the test group zone was reduced by 99.1% as it recorded 0.05mm/min at the 50-minute point when the run-off intensity was highest. The maximum rainfall run-off intensity in the test group zone (Dv=30.35, NSE=0.36) recorded 0.77mm/min and 1.06mm/min in artificial rainfall monitoring and SWMM computer simulation, respectively, at the 70-minute point in both cases. Likewise, the control group zone (Dv=17.27, NSE=0.78) recorded 2.26mm/min and 2.38mm/min, respectively, at the 50-minutes point. Through statistical assessing the significance between the rainfall & run-off simulating systems and the SWMM computer simulations, this study was able to suggest a preliminary design direction for the rainwater run-off reduction performance of the LID system applied with single vegetation. Also, by comprehensively examining the LID system's soil and vegetation models, and analysis methods, this study was able to compile parameter quantification plans for vegetation and soil sectors that can be aligned with a preliminary design. However, physical variables were caused by the use of a single vegetation-based LID system, and follow-up studies are required on algorithms for calibrating the statistical significance between monitoring and computer simulation results.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.3
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• pp.31-38
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• 2010

Various fundamental and practical theories and technologies are needed for the development of Best Management Practices (BMPs) to manage the problems. The objectives of this paper was to investigate the effect of fertilizer and Non-point suource (NPS) pollution discharges from the field. The effect of fertilizer application was measured with respect to 10 % and 20 % slopes, respectively, using artificial rainfall simulator. The effect of fertilizer application on runoff was not significant because the effect of slope and rainfall intensity were overwhelmed. Runoff from 20 % plots was 21 % larger than that from 10 % plots. While groundwater discharge from 10 % plots was about 70 % larger than that from 20 % plots. It was concluded that runoff and groundwater discharge were largely affected by slope. T-N concentration in groundwater was much higher than that in runoff for both 10 % and 20 % plots. While T-P concentration in groundwater was lower than that in runoff. It explained that T-N moved well through soil pores without adsorption and other chemical reactions but T-P was well adsorbed on the surface of soil particles.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.