• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.67-75
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• 2012

Different from other natural hazards including floods, drought advances slowly and spreads widely, so that the preparedness is quite important and effective to mitigate the impacts from drought. Evaluation and forecast the status of drought for the present and future utilizing the meteorological scenario for agricultural drought can be useful to set a plan for agricultural drought mitigation in agriculture water resource management. In this study, drought climate scenario model on the basis of historical drought records for preparing agricultural drought mitigation was developed. To consider dependency and correlation between various climate variables, this model was utilized the historical climate pattern using reference year setting of four drought levels. The reference year for drought level was determined based on the frequency analysis result of monthly effective rainfall. On the basis of this model, drought climate scenarios at Suwon and Icheon station were set up and these scenarios were applied on the water balance simulation of reservoir water storage for Madun reservoir as well as the soil moisture model for Gosam reservoir watershed. The results showed that drought climate scenarios in this study could be more useful for long-term forecast of longer than 2~3 months period rather than short-term forecast of below one month.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.13-21
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• 2007

Recently, very short-term rainfall forecast using radar is required for regional flash flood according to climate change. This research is to evaluate the feasibility of GIS based distributed model using radar rainfall which can express temporal and spatial distribution in actual dam watershed during flood runoff period. Vflo model which was developed Oklahoma university was used as physical based distributed model, and Namgang dam watershed ($2,293km^2$) was applied as study site. Distributed rainfall according to grid resolution was generated by using K-RainVieux, preprocess program of radar rainfall, from JIN radar. Also, GIS hydrological parameters were extracted from basic GIS data such as DEM, land cover and soil map, and used as input data of distributed model(Vflo). Results of this research can provide a base for building of real-time short-term rainfall runoff forecast system according to flash flood in near future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.72-76
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• 2011

본 연구는 레이더 강우 영상에서 추출된 강우 패턴을 인공신경망으로 처리하여 단기 강우 예측을 수행하는 방안을 제시한 것이다. 본 연구에 활용한 CAPPI 영상자료로는 편차 보정과 품질 관리가 이루어지고 있으며 획득이 용이한 기상청 자료를 이용하였으며 CAPPI의 PNG 영상으로부터 강우 패턴을 추출하고, 이를 역전파 알고리즘의 인공신경망 강우 예측 모형에 학습시켜 단기 강우를 예측하기 위한 절차를 제시하였다. 이를 위하여 강우의 시공간적 변화 패턴 추출을 위한 영상 처리와 GIS 자료처리 기법을 제시하였고 이를 인공신경망의 단기 강우 예측 학습과 검증에 적용하여 본 연구에서 제시된 기법의 타당성을 검토하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.145-151
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• 2009

Recently, the rapid development of GIS technology has made it possible to handle a various data associated with spatially hydrological parameters with their attribute information. Therefore, there has been a shift in focus from lumped runoff models to distributed runoff models, as the latter can consider temporal and spatial variations of discharge. This research is to evaluate the feasibility of GIS based distributed model using radar rainfall which can express temporal and spatial distribution in actual dam watershed during flood runoff period. K-DRUM (K-water hydrologic & hydaulic Distributed flood RUnoff Model) which was developed to calculate flood discharge connected to radar rainfall based on long-term runoff model developed by Kyoto- University DPRI (Disaster Prevention Research Institute), and Yondam-Dam watershed ($930km^2$) was applied as study site. Distributed rainfall according to grid resolution was generated by using preprocess program of radar rainfall, from JIN radar. Also, GIS hydrological parameters were extracted from basic GIS data such as DEM, land cover and soil map, and used as input data of distributed model (K-DRUM). Results of this research can provide a base for building of real-time short-term rainfall runoff forecast system according to flash flood in near future.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.572-583
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• 2019

In this study, the atmospheric vertical structure (AVS) associated with summertime (June, July, and August) heavy rainfall in Seoul was classified into three patterns (Loaded Gun: L, Inverted V: IV, and Thin Tube: TT) using rawinsonde soundings launched at Osan from 2009 to 2018. The characteristics of classified AVS and precipitation property were analyzed. Occurrence frequencies in each type were 34.7% (TT-type), 20.4% (IV-type), 20.4% (LG-type), and 24.5% (Other-type), respectively. The mean value of Convective Available Potential Energy (1131.1 J kg^-1) for LG-types and Storm Relative Helicity (357.6 ㎡s^-2) for TT-types was about 2 times higher than that of other types, which seems to be the difference in the mechanism of convection at the low level atmosphere. The composited synoptic fields in all cases showed a pattern that warm and humid southwesterly wind flows into the Korean Peninsula. In the cases of TT-type, the low pressure center (at 850 hPa) was followed by the trough in upper-level (at 500 hPa) as the typical pattern of a low pressure deepening. The TT-type was strongly influenced by the low level jet (at 850 hPa), showing a pattern of connecting the upper- and low-level jets. The result of analysis indicated that precipitation was intensified in the first half of all types. IV-type precipitation induced by thermal instability tended to last for a short term period with strong precipitation intensity, while TT-type by mechanical instability showed weak precipitation over a long term period.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.42-46
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• 2009

최근 국지적 집중호우로 인한 인명과 재산피해가 증가하고 있는 실정이며 이러한 피해를 경감하기 위한 하나의 방책으로써 홍수예경보 시스템 구축의 관심이 늘어나고 있다. 그러나, 기존의 홍수예측 시스템은 강우관측치를 모형의 입력 자료로 홍수유출을 계산하는데, 집중호우와 같은 악기상 조건에서는 관측강우자료를 이용한 유출해석 결과를 이용하여 홍수예경보 시스템을 운영할 경우 예방 대응시간의 부족으로 인해 방재 효율성이 떨어지는 한계성을 지니고 있다. 이와 같은 상황에서 정확한 기상예보를 활용한 기상-수자원 연계기법을 개발하여 홍수예경보 시스템에 적용한다면 악기상 감시예측기술의 향상과 더불어 재해의 방지차원에서 매우 유용한 대책이 될 뿐만 아니라 그 활용성을 극대화 시킨다면 수자원분야의 치수기 홍수예측 등에 매우 유용하게 활용될 수 것이다. 이에 본 연구에서는 모형의 입력으로 활용되는 단기 예측강우의 국내 적용성 여부를 검토하기 위해 30km의 공간해상도를 가진 단기지역예보모델인 RDAPS(Regional Data Assimilation and Prediction System) 예측강우 자료에 대하여 수문학적 정확도 분석을 수행하였으며, 예측강우의 정확도 향상을 위한 편차보정 방법을 개발 적용하였다. 또한 산정된 예측강우를 바탕으로 HEC-1 모델과의 연계방안을 제안하고 이를 이용하여 한강수계 주요 댐유역의 예측유입량을 산정, 댐 운영에 대한 적용성을 판단하고자 한다.