• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.83-93
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• 2020

East Asia, which includes China, Japan, Korea, and Mongolia, is highly impacted by hydroclimate extremes such drought, flood, and typhoon recent year. In 2017, more than 18.5 million hectares of crops have been damaged in China, and Korea has suffered economic losses as a result of severe drought. Satellite-derived rainfall products are becoming more accurate as space and time resolution become increasingly higher, and provide an alternative means of estimating ground-based rainfall. In this study, we verified the availability of rainfall products by comparing widely used satellite images such as Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with ground stations in East Asia. Also, the satellite-based rainfall products were used to calculate the Standardized Precipitation Index (SPI). The temporal resolution is based on monthly images and compared with the past 30 years data from 1989 to 2018. The comparison between rainfall data based on each satellite image products and the data from weather station-based weather data was shown by the coefficient of determination and showed more than 0.9. Each satellite-based rainfall data was used for each grid and applied to East Asia and South Korea. As a result of SPI analysis, the RMSE values of CHIRPS were 0.57, 0.53 and 0.47, and the MAE values of 0.46, 0.43 and 0.37 were better than other satellite products. This satellite-derived rainfall estimates offers important advantages in terms of spatial coverage, timeliness and cost efficiency compared to analysis for drought assessment with ground stations.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2084-2093
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• 2016

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

• KIEAE Journal
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• v.5 no.3
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• pp.17-24
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• 2005

Until today, rainwater management was processed without disposing the peak discharge, which was due to rainfall, to provide stability against flood damage. In this process, the natural hydrologic cycle changed quickly, and because of this, some problems that could harm human beings and the environment arose. These problems need to be addressed accordingly. One of the proposals was to carry out decentralized rainwater management through a natural hydrologic cycle on site, including utilization, infiltration, detention, and retention of rainwater. This study aims to set the direction of applicable decentralized rainwater management to housing complex in Korea. Therefore, spacial properties in urban housing complexes were analysed such as the impervious area-to-land ratio, the green area-to-land ratio, artificial land-to-land ratio etc. As the result of this study, when a housing complex was small and developed by reconstruction, the impervious area, artificial land, the green area in the artificial land-to-land ratio were high. So, direction of decentralized rainwater management of these housing complexes is available to utilize and detain rainwater. On the other hand, those of big housing complexes in land development district were low relatively. So, direction of decentralized rainwater management of these housing complexes is available to infiltrate and evaporate rainwater.

• 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.

• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.238-246
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• 2016

Water purification facilities utilizing three technologies - a detention pond, an artificial wetland and an ecological revetment - were installed in waterfront spaces, including river embankments and watersides that have so far been left neglected, and then their water purification levels were analyzed in this study. The water purification facilities were found to show average removal efficiencies of 48.6% in suspended solid (SS), 30.5% in biochemical oxygen demand (BOD), 18.4% in total nitrogen (T-N) and 27.3% in total phosphorus (T-P) during rainfall. The removal efficiencies during non-rainfall were 33.2% in SS, 28.6% in BOD, 13.7% in T-N and 17.3% in T-P. These results showed that the water purification facilities using a detention pond, an artificial wetland and an ecological revetment can be used as a useful natural water purification technology in in waterfront spaces.

• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.487-494
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• 2005

In order to estimated a reason of soil slope failure new measurement technology is demanded to measure a variation of volumetric water content which is a key physical parameter for understanding the slope failure in the field. In this study a laboratory soil tank test were conducted to use RDB and ADR measurement probes for measuring the variation of volumetric water content. These experiments were compared with two physical parameters as volumetric water content and pressure water head which are estimated to the compacted weathered granite soil under the artificial rainfall, 7.5mm/hour, in the whole of two stages. From the results the variation of volumetric water content and pressure water head is represented to nearly similar travel time.

• Journal of Ginseng Research
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• v.40 no.1
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• pp.90-96
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• 2016

Background: Knowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. Methods: Weather data were measured under the shade and outside the shade at two fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012 seasons to assess temperature and humidity conditions under the shade. An empirical approach was developed and validated for the estimation of leaf wetness duration (LWD) using weather measurements outside the shade as inputs to the model. Results: Air temperature and relative humidity were similar between under the shade and outside the shade. For example, temperature conditions favorable for ginseng growth, e.g., between $8^{\circ}C$ and $27^{\circ}C$, occurred slightly less frequently in hours during night times under the shade (91%) than outside (92%). Humidity conditions favorable for development of a foliar disease, e.g., relative humidity > 70%, occurred slightly more frequently under the shade (84%) than outside (82%). Effectiveness of correction schemes to an empirical LWD model differed by rainfall conditions for the estimation of LWD under the shade using weather measurements outside the shade as inputs to the model. During dew eligible days, a correction scheme to an empirical LWD model was slightly effective (10%) in reducing estimation errors under the shade. However, another correction approach during rainfall eligible days reduced errors of LWD estimation by 17%. Conclusion: Weather measurements outside the shade and LWD estimates derived from these measurements would be useful as inputs for decision support systems to predict ginseng growth and disease development.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.461-468
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• 2019

Background: This study aimed to identify NO₃^--N sources using the stable isotope δ¹⁵N in Chydorus sphaericus (OF Müller), to investigate hydrological characteristics and nutrient states in artificial wetlands near the Nakdong River. Chydorus sphaericus is dominant in wetlands where aquatic plants are abundant, occurring in high density, and is sensitive to wetland water pollution, making it suitable for identification of NO₃^--N sources. Results: NO₃^--N sources for each wetland were strongly dependent on hydrological characteristics. Wetlands with sewage or rainfall/groundwater as their main sources had high levels of NO₃^--N, whereas wetlands with surface water as their main input had comparatively lower levels. Since wetlands with sewage and rainfall/groundwater as their main water sources were mostly detention ponds, their inputs from tributaries or the main river stream were limited and nutrients such as NO₃^--N easily become concentrated. Changes in NO₃^--N levels at each wetland were closely associated with δ¹⁵N of C. sphaericus. Interestingly, regression analysis also showed positive correlation between δ¹⁵N of C. sphaericus and NO₃^--N level. Conclusions: We conclude that the nitrate stable isotope (δ¹⁵N) of C. sphaericus can be used to elucidate the hydrological characteristics of riverine wetlands. This information is important for maintenance and conservation of artificial wetlands at the Nakdong River.