• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.31-43
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• 2017

Recent studies about irrigation water use have focused on agricultural reservoir operation in irrigation period. At the same time, it is significant to store water resource in reservoir during non-irrigation period in order to secure sufficient water in early growing season. In this study, Representative Concentration Pathways (RCP) 4.5, 8.5 scenarios with the Global Climate Model (GCM) of The Second Generation Earth System Model (CanESM2) were downscaled with bias correlation method. Cumulative precipitation during non-irrigation season, October to March, was analyzed. Interaction between cumulative precipitation and carry-over storage was analyzed with linear regression model for ten study reservoirs. Using the regression model, reservoir drought response ability was evaluated with expression of excess and deficiency. The results showed that future droughts will be more severe than past droughts. Especially in case of non-exceedance probability of 10%, drought in southern region seemed to be serious. Nine study reservoirs showed deficiency range from 10% to 55%, which turned out to be vulnerable for future drought. Only Jang-Chan reservoir was secure for early growing season in spite of drought with deficiency of 8% and -2%. The results of this study represents current agricultural reservoirs have vulnerability for the upcoming drought.

• Smart Structures and Systems
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• v.17 no.6
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• pp.859-880
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• 2016

This study aims to establish an effective methodology for the detection of instant damages occurred in cable-stayed bridges with the measurements of cable vibration and structural temperatures. A transfer coefficient for the daily temperature variation and another for the long-term temperature variation are firstly determined to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to decide four upper criteria and four lower criteria for damage detection. With these criteria, the monitoring data for three stay cables of Ai-Lan Bridge are analyzed and compared to verify the proposed damage detection methodology. The simulated results to consider various damage scenarios unambiguously indicate that the damages with cable force changes larger than ${\pm}1%$ can be confidently detected. As for the required time to detect damage, it is found that the cases with ${\pm}2%$ of cable force change can be discovered in no more than 6 hours and those with ${\pm}1.5%$ of cable force change can be identified in at most 9 hours. This methodology is also investigated for more lightly monitored cases where only the air temperature measurement is available. Under such circumstances, the damages with cable force changes larger than ${\pm}1.5%$ can be detected within 12 hours. Even though not exhaustively reflecting the environmental temperature effects on the cable force variation, both the effective temperature and the air temperature can be considered as valid indices to eliminate these effects at high and low monitoring costs.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.25-31
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• 2019

Because of its physical characteristics, earthquake has a great impact on a wide area in a short time, so it needs a resilience based seismic countermeasures to restore the community function. For this reason, in this study, the seismic damages of urban buildings were assessed stochastically by virtual earthquakes using public data information and disaster management program(Ergo-EQ). A geographical map reflecting geological characteristics of the target area was created with the buildings and topographic data in Dalseo-gu, Daegu City. In addition, an integrated database including building characteristics was modified to be linked with the Ergo-EQ program. The seismic damages for the buildings were evaluated through the exceedance probability of four different damage levels. From the damage results, it can be identified not only the seismic damage of each building, but also the major factors affecting earthquake damage.

• Journal of Korea Water Resources Association
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• v.52 no.3
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• pp.173-185
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• 2019

The Korean Peninsula is considered as one of the most typhoon related disaster prone areas. In particular, the potential risk of flooding in coastal areas would be greater when storm surge and heavy rainfall occurred at the same time. In this context, understanding the mechanism of the interactions between them and estimating the risk associated with the concurrent occurrence are of particular interests especially in low-lying coastal areas. In this study, we developed a Poisson-Generalized Pareto (Poisson-GP) distribution based storm surge frequency analysis model to combine the occurrence of the exceedance of a threshold, that is the peaks over threshold (POT), within a Bayesian framework. The storm surge frequency analysis technique developed through this study might contribute to the improvement of disaster prevention technology related to storm surge in the coastal area.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.275-288
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• 2006

This study develops ESP (Ensemble Streamflow Prediction) system by using medium-term numerical weather prediction model which is GDAPS(T213) of KMA. The developed system forecasts medium- and long-range exceedance Probability for streamflow and RPSS evaluation scheme is used to analyze the accuracy of probability forecasts. It can be seen that the daily probability forecast results contain high uncertainties. A sensitivity analysis with respect to forecast time resolution shows that uncertainties decrease and accuracy generally improves as the forecast time step increase. Weekly ESP results by using the GDAPS output with a lead time of up to 28 days are more accurately predicted than traditional ESP results because conditional probabilities are stably distributed and uncertainties can be reduced. Therefore, it can be concluded that the developed system will be useful tool for medium- and long-term reservoir inflow forecasts in order to manage water resources.

• Journal of Environmental Health Sciences
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• v.45 no.3
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• pp.247-257
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• 2019

Objectives: The Korea Ministry of Environment regulates concentrations of hazardous air pollutants (HAPs) through Atmosphere Environmental Standards to protect public health from HAPs. However, simply determining the exceedance of HAP concentrations has several limitations and more comprehensive assessment is required. In addition, integrated risk assessment is needed considering exposure in all microenvironments, including outdoor as well as indoor environments. The purpose of this study was to assess the differences in risk by sub-population groups according to time-activity patterns and reported concentrations, as well as the lifetime risk for Koreans. Methods: In this study, we calculated time-weighted average exposure concentrations for benzene and $PM_{10}$ among preschool-age children, students, housewifes, workers, and the elderly using residential time and concentrations for indoor (house, school or workplace, other), outdoor, and transport by the meta-analysis method. The risk assessments were conducted by excess cancer risk and disease death risk using 1,000,000 Monte-Carlo simulations for probabilistic analysis. Results: Preschool-age children, students, housewifes, workers, and the elderly spent 91.9, 86.0, 79.8, 82.2, and 77.3% of their day in their house, workplace, or school, respectively. The more than 99% excess cancer risk for benzene exceed 1.0E-06 in all sub-populations and lifetime. The acute disease death risk for $PM_{10}$ for housewifes and workers for lifetime were 3.35E-04 and 3.18E-04, and chronic disease death risks were 2.84E-03 and 2.70E-03, respectively. Conclusions: The risk of benzene and $PM_{10}$ by sub-population group and for the lifetime of housewifes and workers were assessed. Benzene showed risky results for this study. All disease death risks of $PM_{10}$ were higher than 1.0E-04 and showed different risks by sub-population. This study can be used as a basis for lifetime exposure and risk assessment to benzene and $PM_{10}$.

• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.103-109
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• 2009

The probabilistic seismic hazard analysis (PSHA) calculates the probability of exceedance of a certain ground motion parameter within a finite period at a site of interest. PSHA is very robust in that it can account for the uncertainties in seismic source, wave passage effect, and seismic site effects and hence, it is the most widely used method in quantifying the future earthquake induced ground vibration. This paper evaluates the applicability of a new PSHA that is alleged to be able to reproduce the results of a conventional PSHA method, but generates a series of earthquake scenarios and corresponding ground motion time histories that are compatible with the scenarios. In the application, a 40,000 year period is simulated, during which 16,738 virtual earthquakes have occurred. The seismic hazard maps are generated from the outputs of the new PSHA. Comparisons with the maps generated by the conventional PSHA method demonstrated that the new PSHA can successfully reproduce the results of a conventional PSHA. The new PSHA may not be very meaningful in itself. However, the real advantage of the method is that it can be used to develop probabilisitic seismic site coefficients. The suite of generated ground motion time histories are used to develop probabilistic site coefficients in the companion paper.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.