• 대한원격탐사학회지
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• 제25권6호
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• pp.465-474
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• 2009

Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2010년도 추계학술대회 초록집
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• pp.203-203
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• 2010

Small hydropower is one of the many types of new and renewable energy, which South Korea is planning to develop, as the country is abundant in endowed resources. In order to fully utilize small hydropower resources, there is a need for greater precision in quantifying small hydropower resources and establish an environment in which energy sources can be discovered using the small hydropower geographic information system. This study has given greater precision to calculating annual electricity generation and installed capacity of small hydropower plants of 117 medium basins by inquiring into average annual rainfall, basin area and runoff coefficient, which is anticipated to promote small hydropower resources utilization. Small hydropower geographic information system was also established by additionally providing base information on quantified small hydropower resources and analysis function and small hydropower generator status, rivers, basin, rainfall gauging station, water level gauging station etc.. Established system of GIS small hydropower energy can be used gather basic information for positive applications of small hydropower energy nationwide.

• 물과 미래
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• 제25권3호
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• pp.79-86
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• 1992

ARF(Areal Reduction Factor) have been developed and used to convert point I-D-F to areal I-D-F in many countries. In Korea, through ARF was calculated in Han river basin by several researchers, it has limit to apply to other regions \ulcorner 새 low density of rainfall gauge station and shortage of data. In this study ARF has developed in areas of high density of rainfall gauge station, Pyungchang river(han river), Wi stream(nakdong river), and Bochung stream(Guem river) basin by fixed-area method. And coefficient of variation of annual mean precipitation was presented to use ARF in othere areas and its applicability was analyzed.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1427-1430
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• 2009

The amount and the continuity of the precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study the eight methods widely used as methods for estimating are compared. The data used in this research is the annual precipitation amount during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station is estimated and the areal average of annual precipitation for 32 years at the Han River basin is calculated.

• Plant Resources
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• 제7권1호
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• pp.36-38
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• 2004

Bacterial grain rot of rice caused by Burkholderia glumae was examined between weather condition and disease incidence. From 1998 to 2000, average disease incidence was 3.0 ％ without difference in survey regions. However, it was related closely to amount of rainfall that disease incidence higher in 1998 and 2000 to 3.0 ％ and 3.6 ％ respectively than 2.3 in 1999 relatively small volum of rainfall season.

• 한국농공학회논문집
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• 제54권4호
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• pp.127-135
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• 2012

지속가능한 수자원 관리 시스템을 위한 수문 예측은 안정적인 장단기 용수 공급에 있어 중요한 과제이며, 이를 위해는 다양한 기후 정보를 이용한 시스템의 평가가 우선되어야 한다. 본 연구에서는 미국 플로리다 템파 지역의 연간 월 강우와 하천 유량 예측을 위해 본 시험지역에 운용되고 있는 유량 모의 시스템 (flow modeling system, FMS)을 소개하고, 관측된 강우 자료를 '최적 예측 강우 시나리오 (the best rainfall forecast)'로 가정하여 FMS의 기후 예측 정보에 대한 활용성을 평가하였다. 연구 결과, 기본적으로 FMS에 의해 예측된 월 강우량 앙상블의 중앙값이 관측 강우량을 잘 재현하는 것으로 나타났다. 강우 예측 모델 입력자료로 사용되는 초기 월 강우량은 2개월까지의 예측에 간섭하며 이 후 예측치는 동일한 범주로 수렴하여 관측자료로 부터 추정된 통계치에 의존하는 것으로 나타났다. 이는 예측 모델이 최대 2개월간의 예측 효용성을 가짐을 의미한다. 월 강우량 앙상블을 이용하여 예측된 하천 유량 앙상블은 4-6개월까지의 예측 효용성을 보였다. 예측된 강우량 대신 실제 관측 월강우 시계열 자료를 유량 예측을 위한 강우 입력자료로 적용한 결과, 예측된 유량의 범주가 현저히 감소하였으며 예측의 불확실성이 감소하는 것으로 나타났다. 본 연구 결과는 시험 지역에 대한 신뢰도 높은 강우 예측 자료의 확보가 기존의 수문 예측 시스템 개선에 기여할수 있다는 것을 보여준다.

• 한국습지학회지
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• 제7권1호
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• pp.81-91
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• 2005

본 연구에서는 하천에서의 흐름과 수질변화를 실시간으로 감시할 수 있는 수문 수질관측시스템을 구축하고자 한다. 이를 통해 하천에서의 오염사고에 대비한 수질감시와 수질변화를 모의할 수 있는 하천관리시스템을 구축하고자 한다. 횡성댐 상류 계천 유역에 시험유역을 선정하였으며, 보다 정확한 수문 및 수질자료를 얻기 위해 우량관측소 3개소, 수위관측소 3개소 및 수질관측소 1개소를 설치하여 실시간 수문 수질관측시스템을 운영하고 있다. 이와 같이 구축된 관측시스템을 통해 강우량, 수위, 유속, 유량 및 수질 등과 같은 자료들을 축적하고, 다양한 분야의 활용을 위해 자유롭게 공개함으로써 관측자료에 대한 활용도를 높이고자 한다.

• 대한토목학회논문집
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• 제12권2호
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• pp.123-130
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• 1992

Geogakakos와 Bras의 일차원 지점 강수량 모형이 전주지점 호우모형으로 적합한지를 검토하였다. 구름 물리학을 토대로 한 이 모형의 기본변수는 운정의 압력, 평균 상승 기류 속도, 운저의 평균 운적직경의 역수값 등인데, 입력변수에 의하여 매개상수화 된다. 매개상수는 Hooke와 Jeeves의 직접 탐색 알고리즘에 의하여 평가되었다. 그 결과 계산 강우량과 실측 강우량과의 평균 자승 오차를 최소화 하는데 평균 상승 기류 속도와 운저 운적직경에 관계된 매개상수가 크게 기여하였다. 이러한 수치실험에서, 계산 총강우량과 실측 총강우량의 편차는 크지 않았으나 시간분포는 상당한 차이를 보였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.