• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.631-641
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• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.22-22
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• 2015

가뭄은 적시에 경보해야 하는 홍수와 달리 진행속도가 느리고 시간적으로 대처할 여유가 있어 진행중일지라도 미리 감지만 한다면 그 피해를 최소화할 수 있다. 이로 인해 미국 등 수문기상 선진국에서는 수문기상 장기예보자료로부터 가뭄전망정보 생산기술을 개발하였으며, 특히 가뭄전망의 정확도 향상을 위해 여러 통계적 보정기법을 적용하고 있다. 국내의 경우 기상청에서 가뭄전망을 목적으로 2011년에 수치예보모델을 이용하여 가뭄전망정보를 생산한바 있으나, 전망정보의 불확실성 문제로 가뭄예보에 활용하는데 한계가 있어 이를 개선할 수 있는 기술개발이 요구되는 실정이다. 본 연구에서는 기후예측자료를 이용하여 가뭄전망정보 생산기술을 개발하고 정확도 개선을 위해 베이지안 기법을 연계하였다. GloSea5 (Global Seasonal forecast model 5) 장기예보자료를 이용하였으며, 베이지안 기법을 통해 과거 관측자료에 대한 사전분포, 모델의 전망정보로부터 우도함수를 유도하여 최종 사후분포를 추정하였다. 베이지안 기법 적용 전 후에 따른 가뭄지수를 산정하였다. 관측자료 기반의 가뭄지수와의 비교분석을 통해 선행기간 및 계절별 가뭄예측 성능을 평가하였으며, 실제 가뭄기간 동안에 가뭄의 재현성을 지역별로 분석하였다. 장기예보자료만을 활용한 기존 가뭄전망에서는 관측 자료가 포함된 1개월 전망에서도 불확실성이 매우 높았지만 베이지안 기법 적용으로 가뭄전망의 정확도가 크게 개선되었다. 특히, 1, 2개월 전망의 시계열 가뭄지수가 관측기반의 가뭄지수의 거동과 매우 유사하게 나타났으며, 지역별로도 베이지안 기법 적용시 실제 가뭄피해 상황을 적절히 재현하는 것으로 나타났다. 국내 가뭄예보에 있어 기후예측정보를 단순활용하기 보다는 베이지안과 같은 통계적 보정기법을 이용하여 가뭄전망정보를 생산하는 것이 바람직하며, 본 연구에서는 가뭄예보업무에 활용될 수 있도록 베이지안 기법에 대한 검증 및 평가를 지속적으로 수행할 계획이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.281-285
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• 2007

본 연구에서는 보다 신뢰성 있고 정확한 정량적 강우예측자료를 생성하기 위하여 레이더강우 및 강우수치예보자료를 합성하는 기법을 제시하였고, 레이더 전처리 및 예측시스템, GIS와 연계한 물리적기반의 분포형모형인 Vflo모형 등 최신 수자원 IT기술을 활용하여 홍수기 돌발홍수에 대응한 초단기 정량적 강우-유출예측을 목적으로 향후 실시간으로 적용 가능한 분포형유출예측시스템의 기반을 구축하고자 하였다. 대상유역은 국지적인 고해상도 지형효과를 고려한 QPM이 개발되어 있는 금강권역의 용담댐유역이며, 예측 강우에 대한 호우사상은 2005년 이후 발생한 3개 강우사상을 대상으로 하였다. 한편, 기상 레이더 자료로부터 산정된 강수량의 수문학적 적용을 위하여 DEM, 토지피복도, 토양도 등의 기본 GIS자료들을 수집 및 구축하였고 물리적기반의 분포형모형(Vflo)의 입력인자로 사용하기 위한 12개의 공간분포형 수문매개변수들을 대표적인 GIS 소프트웨어인 ArcGIS 및 ArcView를 활용하여 추출하였으며, Vflo모형의 현업 적용가능성을 오프라인 상에서 검증해보았다. 모형 검증결과, GIS를 이용한 지형, 토양, 토지피복과 같은 물리적 특성을 사용한 모형의 초기 설정을 향상시킴에 의해 첨두유량, 유출량, 첨두도달시간차 등에서 만족할만한 결과를 보여주었다고 사료된다. 레이더 및 수치예보자료와 합성한 4가지의 형태(QPE, JQPE, QPM, BQPF)의 분포형 입력강우를 이용하여 적용해 본 결과 Nowcasting기법을 이용한 JQPF는 자료의 특성상 초기 1시간30분동안은 비교적 양호한 결과를 얻었으나 3시간 전후로 가면서 예측강우의 질이 저하되기 시작하였으나 QPM을 합성함으로써 생산한 BQPF는 보다 신뢰성있고 양호한 결과를 얻을 수 있었다. 이러한 결과들은 향후 정량적 분포형강우 예측을 이용한 실시간 홍수유출 예측시 댐운영자는 리드타임(홍수선행시간)을 충분히 확보함으로서 안정적이고 예측 가능한 홍수조절을 하는데 도움을 줄 수 있을 것으로 기대된다. 이와 같이 다양한 단기저수지 유입량의 예측정보 제공으로 다목적댐 저수지 운영모형의 효용성을 제고하여 향후 실제 저수지 유입량 예측에 이용함으로써 저수지 단기운영효율 개선에 기여할 수 있을 것으로 사료된다.

• Journal of Wetlands Research
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• v.23 no.2
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• pp.144-153
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• 2021

In recently, the green algae bloom is one of the most severe challenges. The seven days prior prediction is in operation to issues the water quality warning, but it also needs a longer time of prediction to take preemptive measures. The objective of the study is to establish a method to conduct a 3-month prior prediction of Chl-a concentration in the Daechong Lake and tested its applicability as a supplementary of current water quality warning. The historical record of water quality in the Daechong Lake and seasonal forecasting of ECMWF were obtained, and its time-series characteristics were analyzed. The Chl-a forecasting model was established using a correlation between Chl-a concentration and meteorological factor and NARX model, and its efficiency was compared.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.347-359
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• 2010

A simple web-based flood forecasting system using data from stage and rainfall monitoring stations was developed to solve the difficulty that real-time forecasting model could not get the reliabilities because of assumption of future rainfall duration and intensity. The regression model in this research could forecast future water level of maximum 2 hours after using data from stage and rainfall monitoring stations in Daejeon area. Real time stage and rainfall data were transformed from web-sites of Geum River Flood Control Office & Han River Flood Control Office based MS-Excel 2007. It showed stable forecasts by its maximum standard deviation of 5 cm, means of 1~4 cm and most of improved coefficient of determinations were over 0.95. It showed also more researches about the stationarity of watershed and time-series approach are necessary.

• Water for future
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• v.23 no.3
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• pp.341-350
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• 1990

Precise run-off forecasting depends on the ability to predict quantitative rainfall intensity. This study suggests a stochastic model for 1 hour order rainfall prediction. The model simultaneously predicts rainfall intensity at all telemetered rain-gauge locations. All model parameters, velocity and direction of storm movement, radial spectrum, dimensionless time distribution of rainfall, are estimated from telemetered and historical data for the basin being predicted. Also the estimated parameters are based on the previous study. The results are the influence of dimensionless time distributions on the prediction and the model on run-off.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.537-549
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• 2017

This study evaluated the accuracy of rainfall and flood forecasts in Sancheong basin with three rainfall events such as typhoon and stationary front by using LDAPS provided by Korea Meteorological Agency and MSM provided by Japan Meteorological Agency. In the rainfall forecast result, both LDAPS and MSM showed high forecast accuracy for wide-area prediction such as typhoon event, but local-area prediction such as stationary front has a limit to quantitative precipitation forecast (QPF). In the flood forecast result, the forecast accuracy was improved with the increase of the lead time, and it showed the possibility of LDAPS and MSM in the field of rainfall and flood forecast by linking meteorology and water resources.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.225-236
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• 2020

It is not easy to provide sufficient lead time for flood forecast in urban and small mountain basins using on-ground rain gauges, because the time concentration in those basins is too short. In urban and small mountain basins with a short lag-time between precipitation and following flood events, it is more important to secure forecast lead times by predicting rainfall amounts. The Han River Flood Control Office (HRFCO) in South Korea produces short-term rainfall forecasts using the Mcgill Algorithm for Precipitation-nowcast by Lagrangian Extrapolation (MAPLE) algorithm that converts radar reflectance of rainfall events. The Flash Flood Research Center (FFRC) in the Korea Institute of Civil Engineering and Building Technology (KICT) installed a flash flood forecasting system using the short-term rainfall forecast data produced by the HRFCO and has provided flash flood information in a local lvel with 1-hour lead time since 2019. In this study, we addressed the flash flood forecasting system based on the radar rainfall and the assessed the accuracy of the forecasting system for the recorded flood events occurred in 2019. A total of 31 flood disaster cases were used to evaluate the accuracy and the forecast accuracy was 90.3% based on the probability of detection.