• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1369-1372
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• 2008

소수력자원은 신재생에너지 중에서도 온실가스 배출량이 가장 적고 에너지밀도가 매우 높기 때문에 개발할 가치가 큰 청정부존자원으로 평가되고 있다. 강우상태의 변화는 Weibull분포의 축척모수와 형상모수를 인위적으로 변화시켜 소수력발전소의 설계인자들의 변화를 모사하였다. 분석 결과, 소수력발전입지의 수문학적 성능특성은 해당유역의 강우상태에 따라 변하는 것으로 밝혀졌다.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.3
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• pp.140-148
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• 2017

The effectiveness of the first flush treatment system using settling process was evaluated to reduce urban nonpoint source pollutant loads to surface water during storm events. A pilot scale system was constructed and tested in the field and surface runoff samples were collected automatically according to pre-defined conditions. Nine rainfall events were tested and average removal efficiencies of TSS (Total Suspended Solid), TP (Total Phosphorus) and TN (Total Nitrogen) were evaluated as 87.4%, 75.3%, and 43.6%, respectively. Concentration and removal efficiency of pollutants were found to be affected by an amount of rainfall and rainfall intensities of the respective events. This seemed to be caused by the greater particulate fractions of first flushed samples than the samples collected in later time periods during the same rainfall events. The study showed that it is possible to remove a significant portion of the nonpoint source pollutant loads in initial rainfall runoff by using a simple settling process for TSS and TP without requiring additional power or chemicals.

• 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.

• Atmosphere
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• v.23 no.3
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• pp.265-273
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• 2013

To investigate properties of cloud and rainfall occurred at Boseong on 10 July 2012, Raindrop Size distributions (RSDs) and other parameters were analyzed using observation data collected by Micro Rain Radar (MRR) and PARticle SIze and VELocity (PARSIVEL) disdrometer located in the National center for intensive observation of severe weather at Boseong in the southwest of the Korean peninsula. In addition, time series of RSD parameters, relationship between reflectivity-rain rate, and vertical variation of rain rates-fall velocities below melting layer were examined. As a result, good agreements were found in the reflectivity-rain rate time series as well as their power relationships between MRR and PARSIVEL disdrometer. The rain rate was proportional to reflectivity, mean diameter, and inversely proportional to shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD. In comparison of the RSD, as rain rate was increased, the slope of RSD became less steep and the mean diameter became larger. Also, it was verified that reflectivities are classified in three categories (Category 1: Z (reflectivity) > 40 dBZ, Category 2: 30 dBZ < Z < 40 dBZ, Category 3: Z < 30 dBZ). As reflectivity was increased, rain rate was intensified and larger raindrops were existed, while reflectivity was decreased, shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD were increased. We expected that these results will lead to better understanding of microphysical process in convective rainfall system occurred during short-term period over Korean peninsula.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.129-129
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• 2022

국내에서 발생하는 토양침식(soil erosion)은 주로 강우에 의해 발생하며, 이로 인해 농경지 유실, 탁수 발생, 하천 통수능 저하 등 여러 수문학적·환경적 문제가 발생한다. 따라서 유역 내 토양침식 위험지역을 선별하고, 해당 지역의 토양유실 및 유사의 발생량을 산정하는 것은 토양보전 대책 수립 시에 중요한 지표로 활용된다. 침식-유사유출의 물리적 과정은 크게 '강우에 의한 토양 분리(detachment by raindrop)'와 '지표류에 의한 토양 분리(detachment by overlandflow)'로 나눌 수 있으며, 그중 강우에 의한 토양 분리는 수침식(water erosion)의 첫 번째 과정 중 하나로 강우 시 낙하하는 강우 입자들이 갖는 운동에너지가 지표면을 타격할 때 토양체로부터 토양입자가 분리되는 과정이다. 따라서 강우에 의한 토양분리량 산정을 위해서는 강우 운동에너지(rainfall kinetic energy, KE)의 정확한 계산이 요구된다. 그러나 기후 및 지리적 특성 등 여러 조건에 따라 강우 운동에너지는 지역마다 다르게 나타나며, 이로 인해 강우 운동에너지 추정이 매우 어려운 실정이다. 따라서 강우 운동에너지 추정은 주로 강우강도(rainfall intensity, I)와의 관계를 이용한 함수식을 활용한다. 본 연구에서는 대상 지역인 상주지역에 광학우적계(disdrometer)를 설치하여 2020년 6월부터 2021년 12월까지 관측된 37개의 강우 사상에 대하여 KE-I의 관계를 분석하고, 이를 통해 강우 운동에너지식을 도출하였다. 또한, 기존에 국외 및 국내에서 제시된 선형(linear), 멱함수(power-law function), 지수함수(exponential function) 형태의 강우 운동에너지 공식과 본 연구에서 산정된 KE를 비교하였다. 그 결과 비체적 강우 운동에너지에서 Sanchez-Moreno et al. (2012)가 제안한 멱함수 형태의 공식이, 비시간 강우 운동에너지에서 Kinnel (1981)이 제안한 지수함수 형태의 공식이 각각 강우 운동에너지 추정에 통계적으로 유의한 것으로 나타났다.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.525-540
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• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Journal of IKEEE
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• v.15 no.1
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• pp.86-95
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• 2011

In this paper, we propose a testbed system for the crisis management system of the power grid(CMS-PG) using satellite communication network. For the verification of CMS-PG, the proposed system composed of the simulator of satellite communication network and the simulator of phase measurement unit. Proposed satellite communication simulator can evaluate the delay and the robustness of the communication according to the rainfall and the humidity of local site. And the proposed simulator can calculates the voltage stability by hardware implementation using FPGA. Using the proposed testbed system, we adapted its function of crisis management system for the conventional power grid.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.41-49
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• 2018

This study is to analyze and evaluate water resource development potential in North Korea. The study was conducted to analyze selected potential hydropower as an indicator to evaluate water resource development potential. Potential hydropower means theoretical value about the potential capacity of river. It is used to evaluate the amount of development through the hydropower generation. For calculating potential hydropower, monthly average and annual average of rainfall for each river basin were calculated by using the data of 27 rainfall stations in North Korea. As a result of the calculation of theoretical potential hydropower by rainfall in the seven major basins in North Korea, the Aprok River basin was analyzed to be the largest with $7,562.2{\times}10^3kW$. The efficiency and utilization rate of theoretical potential hydraulic power in South Korea and North Korea was 42.3% and 36.2%, respectively. The Daedong River basin's potential hydropower utilization rate is 12.3%, which is the lowest in North Korea. In the case of Daedong River basin, more than 40% of the total population is inhabited, so demand for water and electricity is expected to be the largest. Therefore, the Daedong River basin is considered as a priority area for water resource development. The results of this study are expected to be used as basic data for future water resource development projects and research activities in North Korea.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.47-60
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• 2023

At present, in South Korea, there is a growing concern regarding solar power facilities installed on slopes because they are prone to damage caused by natural disasters, such as heavy rainfall and typhoons. Each year, these solar power facilities experience soil erosion due to heavy rainfall and foundation damage or detachment caused by strong wind loads. Despite these challenges, the interaction between the ground and structures is not adequately considered. Current analyses primarily focus on the structural stability under external loads; the overall facility site's stability-excluding the solar structures-in relation to its surrounding slopes is neglected. Therefore, in this study, we use finite-difference method analysis to simulate the behavior of the foundation and piles to assess changes in lateral displacement and bending stress in piles, as well as the safety factor of sloped terrains, in response to various influencing factors, such as pile diameter, spacing between piles, pile-embedding depth, wind loads, and dry and wet conditions. The analysis results indicate that pile spacing and wind loads significantly influence lateral displacement and bending stress in piles, whereas pile-embedding depth strongly influences the safety factor of sloped terrains. Moreover, we found that under certain conditions, the design criteria in domestic standards may not be met.