• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.170-170
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• 2020

Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.128-128
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• 2022

최근 호우의 빈도와 규모는 증가하는 추세이며 이에 따른 홍수 피해는 많은 피해를 야기하고 있다. 이러한 관점에서 홍수 피해에 대한 선제적 대응을 위한 요소로써 초단시간 강우예측 정보의 중요성은 매우 높다. 특히, 레이더 자료 기반의 강우예측은 수치예보모델과 비교하여 3시간 이내의 짧은 선행시간 이내의 높은 정확도를 갖고 있어 홍수예보에 다수 활용되고 있다. 최근에는 강우자료의 복잡한 관계와 특징을 고려하기 위해 딥러닝 기반의 강우예측 활용 사례가 증가하고 있으나 국내 적용 사례는 적어 관련 연구가 요구되는 실정이다. 본 연구에서는 레이더 강우를 활용한 딥러닝 기반의 강우예측 기법을 제안하고 이에 대한 적용성을 평가하고자 한다. 2차원 레이더 강우자료의 특징과 시계열 특성을 고려하기 위한 심층신경망 구조를 제안하였으며 기존 딥러닝 모형과의 비교를 통해 활용 가능성을 제시하고자 하였다. 적용 대상지역은 한강 유역으로 선정하였다. 정성적 평가를 위해 임계성공지수(CSI)를 활용하여 예측 강우에 대한 정확도를 평가하였으며 정량적 평가를 위해 예측 강우와 관측 강우의 상관관계를 분석하였다. 평가 결과, 제안하는 방법이 기존 모형과 비교하여 예측오차의 범위가 적고 강우의 위치 변화를 잘 반영하는 것으로 나타났다. 본 연구결과는 초단기간 강우예측 자료를 활용하는 홍수예보의 정확도 향상에 기여할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.61-61
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• 2022

최근 머신러닝 기술의 발달에 따라 이를 활용한 레이더 자료기반 강우예측기법이 활발히 개발되고 있다. 기존 머신러닝을 이용한 강우예측모델 개발 관련 연구는 주로 한 지역에 대해 수행되며, 데이터 기반으로 훈련되는 머신러닝 기법의 특성상 개발된 모델이 훈련된 지역에 대해서만 좋은 성능을 보인다는 한계점이 존재한다. 이러한 한계점을 해결하기 위해 사전 훈련된 모델을 이용하여 새로운 데이터에 대해 모델을 훈련하는 전이학습 기법 (transfer learning)을 적용하여 여러 유역에 대한 강우예측모델을 개발하고자 하였다. 본 연구에서는 사전 훈련된 강우예측 모델로 생성적 적대 신경망 기반 기법(Generative Adversarial Network, GAN)을 이용한 미래 강우예측모델을 사용하였다. 해당 모델은 기상청에서 제공된 2014년~2017년 여름의 레이더 이미지 자료를 이용하여 초단기, 단기 강우예측을 수행하도록 학습시켰으며, 2018년 레이더 이미지 자료를 이용한 단기강우예측 모의에서 좋은 성능을 보였다. 본 연구에서는 훈련된 모델을 이용해 새로운 댐 유역(안동댐, 충주댐)에 대한 강우예측모델을 개발하기 위해 여러 전이학습 기법을 적용하고, 그 결과를 비교하였다. 결과를 통해 새로운 데이터로 처음부터 훈련시킨 모델보다 전이학습 기법을 사용하였을 때 좋은 성능을 보이는 것을 확인하였으며, 이를 통해 여러 댐 유역에 대한 모델 개발 시 전이학습 기법이 효율적으로 적용될 수 있음을 확인하였다.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Journal of the Korean GEO-environmental Society
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• v.18 no.12
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• pp.47-57
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• 2017

In this study, a predictive analysis was conducted on sediment disaster hazard area by selecting six research areas (Chuncheon, Seongnam, Sejong, Daejeon, Miryang and Busan) among the urban sediment disaster preliminary focus management area. The models that were used in the analysis were the existing models (SINMAP and TRIGRS) that are commonly used in predicting sediment disasters as well as the program developed through this study (LSMAP). A comparative analysis was carried out on the results as a means to review the applicability of the developed model. The parameters used in the predictions of sediment disaster hazard area were largely classified into topographic, soil, forest physiognomy and rainfall characteristics. A predictive analysis was carried out using each of the models, and it was found that the analysis using SINMAP, compared to LSMAP and TRIGRS, resulted in a prediction of a wider hazard zone. These results are considered to be due to the difference in analysis parameters applied to each model. In addition, a comparison between LSMAP, where the forest physiognomy characteristics were taken into account, and TRIGRS showed that similar tendencies were observed within a range of -0.04~2.72% for the predicted hazard area. This suggests that the forest physiognomy characteristics of mountain areas have diverse impacts on the stability of slopes, and serve as an important parameter in predicting sediment disaster hazard area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.703-714
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• 2017

This paper analyzes the impact of two climate change scenarios on flow rate and water quality of the Yongdam Dam and its basin using CE-QUAL-W2 and SWAT, respectively. Under RCP 4.5 and RCP 8.5 scenarios by IPCC, simulations were performed for 2016~2095, and the results were rearranged into three separate periods; 2016~2035, 2036~2065 and 2066~2095. Also, the result of each year was divided as dry season (May~Oct) and wet season (Nov~Apr) to account for rainfall effect. For total simulation period, arithmetic average of flow rate and TSS (Total Suspended Solid) and TP (Total Phosphorus) were greater for RCP 4.5 than those of RCP 8.5, whereas TN (Total Nitrogen) showed contrary results. However, when averaged within three periods and rainfall conditions the tendencies were different from each other. As the scenarios went on, the number of rainfall days has decreased and the rainfall intensities have increased. These resulted in waste load discharge from the basin being decreased during the dry period and it being increased in the wet period. The results of SWAT model were used as boundary conditions of CE-QUAL-W2 model to predict water level and water quality changes in the Yongdam Dam. TSS and TP tend to increase during summer periods when rainfalls are higher, while TN shows the opposite pattern due to its weak absorption to particulate materials. Therefore, the climate change impact must be carefully analyzed when temporal and spatial conditions of study area are considered, and water quantity and water quality management alternatives must be case specific.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.496-503
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• 2021

By analyzing the characteristics of atmospheric stability in the southern coastal region of the Korean Peninsula in the summer of 2019, a quantitative threshold of atmospheric instability indices was derived for predicting rainfall events in the Korean Peninsula. For this analysis, we used data from all of the 243 radiosonde intensive observations recorded at the Boseong Standard Weather Observatory (BSWO) in the summer of 2019. To analyze the atmospheric stability of rain events and mesoscale atmospheric phenomena, convective available potential energy (CAPE) and storm relative helicity (SRH) were calculated and compared. In particular, SRH analysis was divided into four levels based on the depth of the atmosphere (0-1, 0-3, 0-6, and 0-10 km). The rain events were categorized into three cases: that of no rain, that of 12 h before the rain, and that of rain. The results showed that SRH was more suitable than CAPE for the prediction of the rainfall events in Boseong during the summer of 2019, and that the rainfall events occurred when the 0-6 km SRH was 150 m² s^-2 or more, which is the same standard as that for a possible weak tornado. In addition, the results of the atmospheric stability analysis during the Changma, which is the rainy period in the Korean Peninsula during the summer and typhoon seasons, showed that the 0-6 km SRH was larger than the mean value of the 0-10 km SRH, whereas SRH generally increased as the depth of the atmosphere increased. Therefore, it can be said that the 0-6 km SRH was more effective in determining the rainfall events caused by typhoons in Boseong in the summer of 2019.