• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.717-726
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• 2006

In this study, we developed a rainfall forecasting model using data from radiosonde and rain gauge network and neural networks. The primary hypothesis is that if we can consider the moving direction of the rain generating weather system in forecasting rainfall, we can get more accurate results. We assume that the moving direction of the rain generating weather system is same as the wind direction at 700mb which is measured at radiosonde networks. Neural networks are consisted of 8 different modules according to 8 different wind directions. The model was verified using 350 AWS data and Pohang radiosonde data. Correlation coefficient is improved from 0.41 to 0.73 and skill score is 0.35. Statistical performance measures of the Quantitative Precipitation Forecast (QPF) model show improved output compared to that of rainfall forecasting model using only AWS data.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.254-257
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• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1991-1995
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• 2010

도시화로 인한 토지 피복 상태의 변화로 인한 불투수면적의 증가와 강우강도가 증가함에 따라 지하수로 침투하지 못하고, 바다로 유출되는 강우량이 점차 증가하고 있다. 또한, 인구의 증가와 산업발달에 따른 무분별한 지하수의 사용은 심각한 지하수위의 하강으로 이어지고 있다. 지하수위와 강우량간의 상관관계를 분석하여 지하수의 체계적인 관리 및 운용을 하고자 본 연구를 진행하였다. 본 연구에서는 경상도 지역의 지하수위 관측소와 강우 관측소간의 거리가 10km 이내인 지점을 선정하여, 관측 자료와 분석결과를 토대로 13개 지점을 선정하였다. 침투현상이 침루과정을 거쳐 지하수에 유입되는 과정을 고려하면 강우가 발생한 시점보다 시간이 경과 한 후에 이 지점의 하루에 내린 강우량이 이틀에 걸쳐 지하수위에 영향을 준다고 가정하였고, 1일째의 강우를 실제 강우량의 최대 100%에서 50%까지로 설정하고 2일째에 나머지 강우가 내렸다고 가정하여 각각의 강우이동평균값과 지하수위간의 상관관계를 분석하였다. 또한, 한계침투량을 고려하여 강우이동평균값과 지하수위간의 상관관계를 분석하였다. 그 결과 한계침투량 고려시 상관계수가 0.5 이상인 지점들 중 약 70%가 강우량을 강우사상이 발생한 당일과 명일로 나누었을 때, 상관계수가 높게 나타났다. 그러므로 기존 강우와 지하수위 관측자료만 이용하여 강우이동평균과 지하수위의 상관관계를 분석하는 것 보다 침투율을 고려한 강우이동평균과 지하수위의 상관관계 분석으로 인해 지하수의 체계적인 관리와 분석이 가능할 것으로 사료된다.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.264-277
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• 2021

This study analyzed the relationship between the total precipitable water vapor in the atmosphere and the moving speed of recent typhoons. This study used ground observation data of air temperature, precipitation, and wind speed from the Korea Meteorological Administration (KMA) as well as total rainfall data and Red-Green-Blue (RGB) composite images from the U.S. Meteorological and Satellite Research Institute and the KMA's Cheollian Satellite 2A (GEO-KOMPSAT-2A). Using the typhoon location and moving speed data provided by the KMA, we compared the moving speeds of typhoon Bavi, Maysak, and Haishen from 2020, typhoon Tapah from 2019, and typhoon Kong-rey from 2018 with the average typhoon speed by latitude. Tapah and Kong-rey moved at average speed with changing latitude, while Bavi and Maysak showed a significant decrease in moving speed between approximately 25°N and 30°N. This is because a water vapor band in the atmosphere in front of these two typhoons induced frontogenesis and prevented their movement. In other words, when the water vapor band generated by the low-level jet causes frontogenesis in front of the moving typhoon, the high pressure area located between the site of frontogenesis and the typhoon develops further, inducing as a blocking effect. Together with the tropical night phenomenon, this slows the typhoon. Bavi and Maysak were accompanied by copious atmospheric water vapor; consequently, a water vapor band along the low-level jet induced frontogenesis. Then, the downdraft of the high pressure between the frontogenesis and the typhoon caused the tropical night phenomenon. Finally, strong winds and heavy rains occurred in succession once the typhoon landed.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.691-702
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• 2006

This study introduced a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows Its potential for investigating agricultural drought characteristics. The method was applied to South Korea as a case study. The proposed procedure included Standardized Precipitation Index(SPI) time series, which were linearly transformed by the Empirical Orthogonal Functions(EOF) method. These EOFs were extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.580-585
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• 2009

Harmful Algal Blooms (HABs), caused by Cochlodinium polykrikoides that causative fishery mortality, impact on aquaculture and economic loss appear particularly in summer and fall seasons in the Korean seas. It was studied on characteristics of HABs in the South Sea of Korea by using satellite and in-situ data. The in-situ data encompassed oceanic and meteorological data from July to October 2002-2008 and satellite data from July to October 2002-2006. Chlorophyll concentrations were calculated using Seaviewing Wide Field-of-view Sensor images by an Ocean Color (OC4) algorithm, and HABs were estimated using the Red tide index Chlorophyll Algorithm (RCA). The HAB occurrences were dominant when water temperature was $22.6-28^{\circ}C$ in August. The frequency of the individual numbers during 2002-2008, the HABs more than 1000 cells/ml (alert condition), were 73.57 %. In meteorological data from July to September during 2002-2008, the average precipitation, the mean air temperature, the mean wind speed and direction, and the sunshine were 9.31 mm/day, $24.07^{\circ}C$, 2.34 m/s and easterly, and 1-11 h, respectively. Our results suggest that the upwelling is caused by southwesterly wind in summer season and the Tsushima Warm Current which have influenced on the dispersion and moving of HAB (chlorophyll). In addition, the fresh water from Nakdong River, as the source of nutrients, also influences the occurrence of HABs.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.61-74
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• 2019

In this study, the SPI (Standardized Precipitation Index) of meteorological drought and SDI (Streamflow Drought Index) of hydrological drought for 1, 3, 6, 9, and 12 months duration were estimated to analyse the characteristics of drought using rainfall and dam inflow data for Chungju dam ($6,661.8km^2$) with 31 years (1986-2016) and Boryeong dam ($163.6km^2$) watershed with 19 years (1998-2016) respectively. Using the estimated SPI and SDI, the drought forecasting was conducted using seasonal autoregressive integrated moving average (SARIMA) model for the 5 durations. For 2016 drought, the SARIMA had a good results for 3 and 6 months. For the 3 months SARIMA forecasting of SPI and SDI, the correlation coefficient of SPI3, SPI6, SPI12, SDI1, and SDI6 at Chungju Dam showed 0.960, 0.990, 0.999, 0.868, and 0.846, respectively. Also, for same duration forecasting of SPI and SDI at Boryeong Dam, the correlation coefficient of SPI3, SPI6, SDI3, SDI6, and SDI12 showed 0.999, 0.994, 0.999, 0.880, and 0.992, respectively. The SARIMA model showed the possibility to provide the future short-term SPI meteorological drought and the resulting SDI hydrological drought.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.5
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• pp.32-40
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• 2006

This study's objects are to suggest effective forest community-level management measures by identifying the vulnerable forest vegetation communities types to climate change through a comparative analysis with present forest communities identified and delineated in the Actual Vegetation Map. The methods of this study are to classify the climatic life zones based on the correlative climate-vegetation relationship for each forest vegetation community, the Holdridge Bio-Climate Model was employed. This study confirms relationship between forest vegetation and environmental factors using Pearson's correlation coefficient analysis. Then, the future distribution of forest vegetation are predicted derived factors and present distribution of vegetation by utilizing the multinomial logit model. The vulnerability of forest to climate change was evaluated by identifying the forest community shifts slower than the average velocity of forest moving (VFM) for woody plants, which is assumed to be 0.25 kilometers per year. The major findings in this study are as follows : First, the result of correlative analysis shows that summer precipitation, mean temperature of the coldest month, elevation, soil organic matter contents, and soil acidity (pH) are highly influencing factors to the distribution of forest vegetation. Secondly, the result of the vulnerability assessment employing the assumed velocity of forest moving for woody plants (0.25kmjyear) shows that 54.82% of the forest turned out to be vulnerable to climate change. The sub-alpine vegetations in regions around Mount Jiri and Mount Seorak are predicted to shift the dominance toward Quercus mongolica and Pinus densiflora communities. In the identified vulnerable areas centering the southern and eastern coastal regions, about 8.27% of the Pinus densiflora communities is likely to shift to sub-tropical forest communities, and 3.38% of the Quercus mongolica communities is likely to shift toward Quercus acutissima communities. In the vulnerable areas scattered throughout the country, about 8.84% of the Quercus mongolica communities is likely to shift toward Pinus densiflora communities due to the effects of climate change. The study findings concluded that challenges associated with predicting the future climate using RCM and the assessment of the future vulnerabilities of forest vegetations to climate change are significant.