• Journal of Soil and Groundwater Environment
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• v.17 no.2
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• pp.37-46
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• 2012

This study was conducted to identify groundwater recharge and discharge amounts of a representative urban-rural composite area located in Yongin city, Kyounggi-do, Korea. Groundwater recharge would be affected by mainly two processes in the study area: rainfall and leakage from public water pipelines including water-supply and sewage system. Groundwater recharge rate was estimated to be 13.5% by applying annual groundwater level data from two National Groundwater Monitoring Stations to the master regression curve method. Subsequently, the recharge amounts were determined to be $13,253{\times}10^3m^3/yr$. Leakage amounts from water-supply and sewage system were estimated to be $3,218{\times}10^3$ and $5,696{\times}10^3m^3/yr$, respectively. On the whole, a total of the recharge amounts was $22,167{\times}10^3m^3/yr$, of which 60% covers rainfall recharge and 40% pipeline leakage. Groundwater discharge occurred through three processes in the composite area: baseflow, well pumping, and discharge from urban infrastructure including groundwater infiltration into sewage pipeline and artificial extraction of groundwater to protect underground facilities from submergence. Discharge amounts by baseflow flowing to the Kiheung agricultural reservoir and well pumping were estimated to be $382{\times}10^3$ and $1,323{\times}10^3m^3/yr$, respectively. Occurrence of groundwater infiltration into sewage pipeline was rarely identified. Groundwater extraction amounts from the Bundang subway line as an underground facility were identified as $714{\times}10^3m^3/yr$. Overall, a total of the discharge amounts was determined to be $2,419{\times}10^3m^3/yr$, which was contributed by 29% of artificial discharge. Even though groundwater budget of the composite area was identified to be a surplus, it should be managed for a sound groundwater environment by changing deteriorated pipelines and controlling artificial discharge amounts.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.2 s.17
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• pp.9-16
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• 2005

This paper presents the use of an Artificial Neural Network (ANN) as a viable means of forecasting Inundation Damage Area (IDA) in many watersheds. In order to develop the forecasting model with various environmental factors, we selected 108 watershed areas in South Korea and collected 49 damage data sets from 1990 to 2000, of which each set is composed of 27 parameters including the IDA, rainfall amount, and land use. After successful training processes of the ANN, a good agreement (R=0.92) is obtained (under present conditions) between the measured values of the IDA and those predicted by the developed ANN using the remaining 26 data sets as input parameters. The results indicate that the inundation damage is affected by not only meteorological information such as the rainfall amount, but also various environmental characteristics of the watersheds. So, the ANN proves its present ability to predict the IDA caused by an event of complex factors in a specific watershed area using accumulated temporal-spatial information, and it also shows a potential capability to handle complex non-linear dynamic phenomena of environmental changes. In this light, the ANN can be further harnessed to estimate the importance of certain input parameters to an output (e.g., the IDA in this study), quantify the significance of parameters involved in pre-existing models, and contribute to the presumption, selection, and calibration of input parameters of conventional models.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.417-423
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• 2018

It analyzed the efficiency of the runoff reduction of artificial reservoir by analyzing the influent and effluent of reservoir located downstream of the livestock area. Production of non point pollutants in livestock feeding areas, which is located at steep slope land, was mainly due to first flushes. Suspended Solid concentration of influent increased due to amount of rainfall, and T-P also increased over four times and 30 % of total nitrogen increased on average compared to those of dry season. While the concentration of nitrate nitrogen showed little variation, ammonia nitrogen increased over two times. The storage style nonpoint reduction facility showed the highest removal efficiency of 53 % for total phosphorus in dry weather, when the removal efficiency was 37 % for suspended solids, 10% for organic compounds, and 5 % for total nitrogen. Since algal bloom grows due to eutrophication in summer, the minus removal efficiencies of nitrogen concentration through the reservoir occurred with high frequency. Removal efficiency decreased during rainfall, showing 60 % for supended solids, and 22 % for total phosphorus. While having over nine times of capacity than the standard of non-point removal facility from Ministry of Environment, it was impounded with water during rainy season, showing not enough nonpoint removal efficiency, which indicates that maintenance is also an important factor to the nonpoint removal efficiency.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.37-41
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• 2009

본 연구에서는 티센망을 이용한 면적강우량 산정방법의 대안으로서 최근 들어 수자원공학 분야에의 활용성이 커지고 있는 고해상도 기상레이더의 반사도자료(dBZ)를 활용하여 면적강우량을 산정하였다. 또한 이렇게 산정된 레이더 면적강우량을 티센망으로써 산정된 면적강우량과 비교하여 그 유용성을 판단하였다. 연구지역으로는 소양강댐 유역을 선정하였으며, 연구기간은 2008년 가장 강한 강우를 보였던 상위 5개의 사상을 선정하였다. 본 연구에서는 레이더 반사도를 강우강도로 변환시키는 과정은 인공신경망(artificial neural network, ANN) 중에서 일반적으로 널리 사용되고 있는 다층 퍼셉트론 인공신경망 모형을 적용하였다. 연구방법으로는 선택된 4개의 인자를 입력노드에 넣어 인공신경망을 학습시킨 후 연구지역 내 10개 AWS 지상관측소의 강우량을 추정하여 정확도를 비교 분석하였다. 이를 바탕으로 최종적으로 레이더 면적강우량을 산정하여 기존의 티센망을 이용한 면적강우량과 그 값을 비교하였다. 그 결과 인공신경망을 이용한 레이더 강우량의 경우, 평균제곱오차(mean square error, MSE) 및 상관계수(correlation coefficient, CC)가 매우 양호한 값을 보였다. 또한 유역 내 레이더 면적강우량이 티센망을 이용한 면적강우량에 비하여 약 $7%^{\sim}19%$ 정도 차이가 발생함을 확인하였으며, 레이더 면적강우량이 티센망을 이용한 면적강우량에 비하여 더 정확한 면적강우량을 산정할 수 있다고 판단된다.

• Journal of Industrial Technology
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• v.29 no.A
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• pp.55-60
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• 2009

Stabilization treatment is one of processes for wastes and their components to reduce their toxicity and migration rates to surroundings. Inorganic binders such as calcium hydroxide, blast furnace slag and red mud were tested for their potential applicability to in-situ stabilization of heavy metal contaminated tailings in the abandoned metal mines. Columns(150mm dia. ${\times}$ 450mm length) filled with mixtures of inorganic binders and tailing from the Geumjang mine with various mixing ratios of binders to tailings, 5%, 7% and 9% were applied artificial rainfall tests for 28 days. Effluents from columns filled with calcium hydroxide and tailing showed high pH's of ~12.5 and a increasing trend of concentration in Pb and Zn with a significant decrease in permeability in terms of elapsed days. Those with burning slag and tailing showed pH's of ~8.5 and significantly low concentrations in heavy metals with a stable permeability. In case of red mud, effluents showed significantly low concentrations in heavy metals but a decreased permeability with pH's of ~10.5. Conclusively, this basic study suggests burning furnace slag be a potential stabilizer for effective treatment of heavy metal contaminated mine tailings.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.98-99
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• 2013

The amount of surface chlorides of architectural structure in incoming salt environment depends on the characteristics of distribution of incoming salt in atmosphere. Therefore, many researches are being conducted on deducting the correlation between incoming salt amount attached to the surface of real structure and that of atmosphere after quantitative measurement. However, in real environment, these studies are somewhat far fetched. That is because incoming salt in atmosphere are changed by various climatic conditions and in the case of the structures surface, attached incoming salt may be carried away due to the rainfall. Therefore, this study aims to draw an improved proportional relation between the amount of sodium chloride in atmosphere and that attached to the surface of architectural structures by measuring the amount attached to each architectural material using artificial incoming salt generator that can control various climatic variables that can be caused in real environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.320-320
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• 2019

유역의 수문 자료를 정확하게 분석하는 것은 수리 구조물을 효율적으로 운영하기 위한 중요한 요소이다. 인공신경망(Artificial Neural Networks, ANNs) 모형은 입 출력 자료의 비선형적인 관계를 해석할 수 있는 모형으로 강우-유출 해석 등 수문 분야에 다양하게 적용되어 왔다. 이후 기존의 인공신경망 모형을 연속적인(sequential) 자료의 분석에 더 적합하도록 개선한 회귀신경망(Recurrent Neural Networks, RNNs) 모형과 회귀신경망 모형의 '장기 의존성 문제'를 개선한 장단기메모리(Long Short-Term Memory Networks, 이하 LSTM)가 차례로 제안되었다. LSTM은 최근에 주목받는 딥 러닝(Deep learning) 기법의 하나로 수문 자료와 같은 시계열 자료의 분석에 뛰어난 성능을 보일 것으로 예상되며, 수문 분야에서 이에 대한 적용성 평가가 요구되고 있다. 본 연구에서는 인공신경망 모형과 LSTM 모형으로 유출량을 모의하여 두 모형의 성능을 비교하고 향후 LSTM 모형의 활용 가능성을 검토하고자 하였다. 나주 수위관측소의 수위 자료와 인접한 기상관측소의 강우량 자료로 모형의 입 출력 자료를 구성하여 강우 사상에 대한 시간별 유출량을 모의하였다. 연구 결과, 1시간 후의 유출량에 대해서는 두 모형 모두 뛰어난 모의 능력을 보였으나, 선행 시간이 길어질수록 LSTM의 정확성은 유지되는 반면 인공신경망 모형의 정확성은 점차 떨어지는 것으로 나타났다. 앞으로의 연구에서 유역 내 다양한 수리 구조물에 의한 유 출입량을 추가로 고려한다면 LSTM 모형의 활용성을 보다 더 확장할 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.330-330
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• 2022

우리나라에서 발생하는 기상 재해 현상은 주로 태풍, 집중호우, 장마 등 인명 및 경제적인 피해가 크며, 단기간에 국지적으로 나타난다. 현재 재해 감시 및 예보는 주로 종관기상관측체계를 이용하고 있다. 하지만, 우리나라의 복잡한 지형, 인구 밀집 지형, 관측 시기가 일정하지 않은 지형과 같은 조건에서 미계측 자료 및 지역이 다수 존재 때문에 강수의 공간 분포와 강도에 대한 정밀한 정보를 제공하지 못하는 실정이다. 최근 광범위한 관측영역과 공간 분해능의 개선, 자료추출 알고리즘의 개발로 전세계적으로 위성영상 기반 기상관측 자료의 활용성이 증대되고 있다. 본 연구에서는 한반도 지역의 지상 관측데이터와 전지구 격자형 위성 강우자료를 비교하여 한반도의 적용성을 분석하고자 한다. 다양한 위성영상 기반 기상자료인 Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) 4개의 강우위성영상을 수집하여, 1991년부터 2020년까지 30년 데이터를 활용하였다. 강수량 변동성 비교를 위하여 기상청의 종관기상관측장비 (Automated Synoptic Observation System, ASOS), 자동기상관측시설 (Automatic Weather System, AWS) 데이터와 상관 분석을 수행하고, 강우위성영상의 국내 적합성을 판단하고자 한다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.188-196
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• 2023

Wheat (Triticum aestivum L.) is an important crop in Korea, with a per capita consumption of 31.6 kg in 2019. In the southern region, wheat is grown after paddy rice, and it is harvested during the rainy season in mid-June. This timing, in combination with high humidity and untimely rainfall, activates the enzyme alpha-amylase, which breaks down starch in the wheat grains. As a result, sprouted grains have lower quality and value for flour. However, seeds that absorb water before sprouting are expected to maintain better quality. The aim of the study was to identify the critical period during wheat maturation when rainfall has the greatest impact on grain quality, to prevent price declines due to quality deterioration. Two wheat cultivars, Jokyoung and Hwanggeumal, were grown in a speed breeding room, and artificial rainfall was applied at different times after heading (30, 35, 40, 45, 50, and 55 days). The proportion of vitreous grains decreased from 40 to 55 days after heading (DAH). Both cultivars had chalky grain sections from 35 DAH, with Hwanggeumal having a higher proportion of vitreous grains. Starch degradation was observed using FE-SEM (Field Emission Scanning Electron Microscope) at 40 DAH for Jokyoung and 50 DAH for Hwanggeumal. Color measurements indicated increased L and E values from 40 DAH, with rain treatment at 55 DAH leading to a significant increase in L values for both cultivars. Ash content increased at 45 DAH, whereas SDSS decreased at 35 DAH. Overall, grain quality from 40 DAH until harvest was found to be affected to the greatest extent by direct exposure of the spikes to moisture. Red wheat showed better quality than white wheat. These findings have implications for the cultivation of high-quality wheat and can guide future research efforts in this area.