• Spatial Information Research
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• v.23 no.1
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• pp.19-31
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• 2015

Due to the global warming and unstable abnormal climate changes, water resources differences between regions and water shortage are occurring. Therefore, the water resources management is becoming more important for the stable securement of future water supply and demand. Researches on Smart Water Grid (SWG), which is considered as a new method, that can stably secure and maintain the water resources, are actively being conducted but it is still in infancy. Thus, this study aimed to design SWG simulator based on GIS in order to provide integrated water information in web environment. The user's requirements were analyzed for system development and important functions such as SWG current situation checking, future prediction, filtration plant situation checking functions were designed and data expression techniques using GIS and HTML5 were applied to enhance the understanding of the users. Also, when the emergency situations occurred, the solving process of the situations are reproduced to check the solution process using scenario reproduction functions. Use-case, class, sequence diagram, which are a design for real system development and defines the system usage contents of users, were written, and the story board was written to check the final development contents. This study designed a SWG simulator in order to support the water maintenance reacting to climate changes. The development of system is expected to help securing information to deal with emergency situations such as water shortage and help the decision maker to make decision through reproduction of scenario. The major functions were designed for the convenience of water resource manager and producer but new contents for consumers must be developed to enable duplex information transmission.

• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.236-243
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• 2007

Fluorescence measurements of dissolved organic matter (DOM) have the superior advantages over other analysis tools for the applications to water quality management due to their rapid analysis. It is known that protein-like fluorescence characteristics are well corelated with microbial activities and biodegradable organic matter. In this study, potential biochemical oxygen demand (BOD) predictor were explored using the fluorescence peak intensities and/or the integrated fluorescence intensities derived from synchronous fluorescence spectra and the first derivative spectra of river samples. A preliminary study was conducted using a mixture of a river and a treated sewage to test the feasibility of the approach. It was demonstrated that the better BOD predictor can be derived from synchronous fluorescence spectra and the derivatives when the difference between the emission and the excitation wavelengths (${\Delta}{\gamma}$) was large. The efficacy of several selected fluorescence parameters was rivers in Seoul. The fluorescence parameters exhibited relatively good correlation coefficients with the BOD values, ranging from 0.59 to 0.90. Two parameters were suggested to be the optimum BOD predictors, which were a fluorescence peak at a wavelength of 283 nm from the synchronous spectrum at the ${\Delta}{\gamma}$ value of 75 nm, and the integrated fluorescence intensity of the first derivatives of the spectra at the wavelength range between 245 nm and 280 nm. Each BOD predictor showed the correlation coefficients of 0.89 and 0.90, respectively. It is expected that the results of this study will provide important information to develop a real-time efficient sensor for river BOD in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.345-352
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• 2017

The average global temperature on Earth has increased by about $0.85^{\circ}C$ since 1880 due to the global warming. The temperature increase affects hydrologic phenomenon and so the world has been suffered from natural disasters such as floods and droughts. Therefore, especially, in the aspect of water deficit, we may require the accurate prediction of water demand considering the uncertainty of climate in order to establish water resources planning and to ensure safe water supply for the future. To do this, the study evaluated future water balance and water deficit under the climate change for Anseong river basin in Korea. The future rainfall was simulated using RCP 8.5 climate change scenario and the runoff was estimated through the SLURP model which is a semi-distributed rainfall-runoff model for the basin. Scenario and network for the water balance analysis in sub-basins of Anseong river basin were established through K-WEAP model. And the water demand for the future was estimated by the linear regression equation using amounts of water uses(domestic water use, industrial water use, and agricultural water use) calculated by historical data (1965 to 2011). As the result of water balance analysis, we confirmed that the domestic and industrial water uses will be increased in the future because of population growth, rapid urbanization, and climate change due to global warming. However, the agricultural water use will be gradually decreased. Totally, we had shown that the water deficit problem will be critical in the future in Anseong river basin. Therefore, as the case study, we suggested two alternatives of pumping station construction and restriction of water use for solving the water deficit problem in the basin.

• Journal of the Korea Organic Resources Recycling Association
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• v.15 no.4
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• pp.138-146
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• 2007

There are still lots of areas where are covered with combined sewer pipes in Seoul. All buildings within those areas are equipping septic tanks which take part in separating solids from flushing water of chamber pots. Septic tanks legally demand emptying and cleaning the those inner bodies, resulting the generation of sludge which should be purified using the specified treatment plants, as one of environmental infrastructures. Scale of treatment plants for septic tank sludge are affected by sludge volume generated from cleaning, which give emphasis to adequate estimation of sludge volume in the future. This study aimed to define prediction tools for sludge volume. Among various parameters, floor area of building is most reasonable one to estimate the quantity of cleaning sludge, showing increasing gradually up to 13,149kL a day in 2020. Using same parameter also are able to assess the amount of BOD in the cleaning sludge.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.639-655
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• 2023

Recently, there has been a growing demand for underground space, leading to the utilization of earth retaining walls for deep excavations. Earth retaining walls are structures that are susceptible to displacement, and their measurement and management are carried out in accordance with the standards established by the Ministry of Land, Infrastructure, and Transport. However, managing displacement through measurement can be considered similar to post-processing. Therefore, in this study, we not only predicted the horizontal displacement of a retaining wall with ground anchors installed using machine learning, but also analyzed the impact of the prediction model based on data scaling and data splitting methods while learning measurement data using machine learning. Custom splitting was the most suitable method for learning and outputting measurement data. Data scaling demonstrated excellent performance, with an error within 1 and an R-squared value of 0.77 when the anchor tensile force and water pressure were standardized. Additionally, it predicted a negative displacement compared to a model that without scaling.

• Journal of the Korean Society of Marine Environment & Safety
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• v.15 no.2
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• pp.91-98
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• 2009

The long-term water-quality change of Asan Bay by the influx of polluted disposal water was predicted through a simulation with an Eco-hydrodynamic model. Eco-hydrodynamic model is composed of a multi-level hydrodynamic model to simulate the water flow and an ecosystem model to simulate water quality. The water quality simulation revealed that the COD(Chemical Oxygen Demand), dissolved inorganic nitrogen(DIN) and dissolved inorganic phosphorus(DIP) are increased at 5 stations for the subsequent 6 months after the influx of the effluent. COD, DIN and DIP showed gradual decreases in concentration during the period of one to two years after the increase of last 6 months and reached steady state for next three to ten years. Concentration levels of COD, DIN, and DIP showed the increase by the ranges of $11{\sim}67%$, $10{\sim}67%$, and $0.5{\sim}7%$, respectively, which represents that the COD and DIN are the most prevalent pollutants among substances in the effluent through the sewage treatment plant. The current water quality of Asan Bay based on the observed COD, TN and TP concentrations ranks into the class II of the Korean standards for marine water quality but the water quality would deteriorate into class III in case that the disposal water by the sewage plant is discharged into the Bay.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.246-256
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• 2011

Due to the increasingly aging population, there is a rising demand for assistive living technologies for the elderly to ensure their health and well-being. The elderly are mostly chronic patients who require frequent check-ups of multiple vital signs, some of which (e.g., blood pressure and blood glucose) vary greatly according to the daily activities that the elderly are involved in. Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of chronic symptoms in the elderly. In this paper, recent development of Wearable Intelligent Systems for e-Health (WISEs) is reviewed, including breakthrough technologies and technical challenges that remain to be solved. A novel application of wearable technologies for transient cardiovascular monitoring during water drinking is also reported. In particular, our latest results found that heart rate increased by 9 bpm (P < 0.001) and pulse transit time was reduced by 5 ms (P < 0.001), indicating a possible rise in blood pressure, during swallowing. In addition to monitoring physiological conditions during daily activities, it is anticipated that WISEs will have a number of other potentially viable applications, including the real-time risk prediction of sudden cardiovascular events and deaths.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.294-294
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• 2019

The system for predicting flood of river at Flood Control Office is made up of a rainfall-runoff model and FLDWAV model. This system is mainly operating to predict the excess of the flood watch or warning level at flood forecast points. As the demand for information of the management and operation of riverside, which is being used as a waterfront area such as parks, camping sites, and bike paths, high-level forecasts of watch and warning at certain points are required as well as production of lowland flood forecast information that is used as a waterfront within the river. In this study, a technology to produce flood forecast information in lowland areas of the river used as a waterfront was developed. Based on the results of the 1D hydraulic analysis, a model for performing spatial operations based on high resolution grid was constructed. A model was constructed for Andong district, and the inundation conditions and level were analyzed through a virtual outflow scenarios of Andong and Imha Dam.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.