• Journal of Forest and Environmental Science
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• v.17 no.1
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• pp.18-28
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• 2001

This study was carried out to investigate the characteristics of water quality variations by stand water balance in YangPyong-Gun Gejung-Lee small forest watershed. Water quantity. pH, $Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$ were monitored in open rainfall for one unit storm and long-term stream water in small forest watershed from January. 1998 to December. 1999. The results were summarized as follows: The runoff rate was 46.4% in 1998 and 52.2% in 1999. The average pH values of rainfall were 4.8 to 6.2 and those of stream water were 6.4 to 7.1 in small forest watershed. Total amount of input anion and cation values (kg/ha) in rainfall were $SO_4{^{2-}}>NO_3{^-}>Ca^{2+}>NH_4{^+}>Cl^->Na^+>K^+>Mg^{2+}$ and in stream water were $NO_3{^-}>Ca^{2+}>SO_4{^{2-}}>Na^+>Cl^->K^+>Mg^{2+}>NH_4{^+}$ in the order, respectively. The dissolved $NH_4{^+}$ was stored 5.29kg/ha and output of the other contents were more flow than input in small forest watershed.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.3
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• pp.315-321
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• 2010

Greenhouse soil cultivated with excessive compost and chemical fertilizer has been an issue to deteriorate soil and water quality in the environment. The objective of this study was to evaluate the nutrient outflow by desalting method, flooding soil surface, after vegetable cropping in greenhouse soils. Field experiment from July to September 2008, was conducted to quantify greenhouse locations, i.e. alluvial plain and local valley. The changes of desalinization in both locations were higher as the amounts of irrigated and drained water were increased. Particularly, the ratio of desalinization in alluvial plain was much higher (66.7%) than the one in local valley (45.6%). However, $NH_4$-N contents of local valley soil during the flooding were higher than in those of alluvial plain. This was caused by high total nitrogen and organic matter in local valley soil than those in alluvial plain soil. With comparing to the input and output loads of T-N and T-P in greenhouses with local valley and alluvial plain soils, the output loads of nutrients were larger than the input loads of nutrients. This result showed that the flooding soil surface can be a good treatment to desalinize greenhouse soils. However, this conclusion remained that the flooding water containing high N and P concentrations might cause the secondary effect on the quality of streams and groundwater since excessive nutrient concentrations can be the main cause of eutrophication problem in aquatic environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.2
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• pp.96-108
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• 2011

Going abreast of high public concerns on the environment, the need of environmental modeling has been increased to assess the impact of space exploitation of environment. GIS offers potential solutions to the many problems encountered during water-quality modeling. But there are also many problems associated with the modeling. The preparation of necessary parameters for the modeling can be complicated. Also, the results from one model can be different from each other even the same area is analyzed. This paper aims to develop the data processing system to couple the Qual2E and HyGIS in which Qual2E input and output data files can be created, modified and processed using HyGIS and assess the performance of the system. A structural analysis and standardization of modeling are conducted to identify data flow and processing of Qual2E. Algorithms of the defined processors are designed and developed as component modules. The data model of HyGIS-Qual2E is designed, and GUI(Graphical User Interface) is developed using Visual Basic 6.0 and GDK.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of environmental and Sanitary engineering
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• v.8 no.2
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• pp.25-48
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• 1993

International concern over the environmental pollution is ever increasing, and diversified countermeasures must be devised in Korea also. Global trend, damages, problems and countermeasures with respect to issues mentioned in the Rio Declaration, such as prevention of ozone layer destruction, reduction of migratory atmospheric pollution between neighboring countries, and prevention of global greenhouse effect, were discussed in this report. Conclusion of the report is summarized as follows : A. Measurement, Planning and Monitoring (1) Development and implementation of a global network for measurement and monitoring from the global aspects such factors as related to acid rain(Pioneer substances, pH, sulfate, nitrate), effect of global temperature(Air temperature, $CO_2$, $CH_4$, CFC, $N_2O$) and destruction of ozone layer($CFC_S$). (2) Establishment of network system via satellite monitoring movement of regional air mass, damage on the ozone layer and ground temperature distribution. B. Elucidation of Present State (1) Improvement and development of devices for carbon circulation capable of accurately forecasting input and output of carbon. (2) Developmental research on chemical reactions of greenhouse gas in the air. (3) Improvement and development of global circulation model(GCM) C. Impact Assessment Impact assessment on ecosystem, human body, agriculture, floodgate, land use, coastal ecology, industries, etc. D. Preventive Measures and Technology Development (1) Development and consumption of new energy (2) Development of new technology for removal of pioneer substances (3) Development of substitute matter for $CFC_S$ (4) Improvement of agriculture and forestry means to prevent the destruction of ozone layer and the greenhouse effect of the globe (5) Improvement of housing to prevent the destruction of ozone layer and the greenhouse effect of the globe (6) Development of new technology for probing underground water (7) Preservation of forest (8) Biomass 5. Policy Development (1) Development of strategy model (2) Development of long term forecast model (3) Development of penalty charge effect and expense evaluation methods (4) Feasibility study on regulations By establishing the above mentioned measures for environmentally sound and sustainable development to establish the right to live for humankind and to preserve the one and only earth.

• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.319-327
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• 2013

The objective of this research was to develop a relational database management system(RDBMS) to collect, manage and analyze data on agricultural non-point source(NPS) pollution. The system consists of the relational database for agricultural NPS data and data process modules. The data process modules were composed of four sub-modules for data input, management, analysis, and output. The data collected from the watershed of the upper Cheongmi stream and Geunsam-Ri were used in this study. The database was constructed using Apache Derby with meteorological, hydrological, water quality, and soil characteristics. Agricultural NPS-Data Management System(ANPS-DMS) was developed using Oracle Java. The system developed in this study can deal with a variety of agricultural NPS data and is expected to provide an appropriate data management tool for agricultural NPS studies.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.2
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• pp.73-84
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• 1992

The TIDE, finite element model for the simulation of tidal flow in shallow sea was tested for its applicability at the Saemangeum area. Several pre and post processors were developed to facilitate handling of the complicated and large amount of input and output data for the model developed. Also an operation scheme to run the model and the processors were established. As a result of calibration test using the observed data collected at 9 points within the region, linearlized friction coefficients were adjusted to be ranged 0.0027~0.0072, and water depths below the mean sea level at every nodes were changed to be increased generally by 1 meter. Comparisons of tidal velocities between the observed and the simulated for the 5 stations were made and obtained the result that the average relative error between simulated and observed tidal velocities was 11% for the maximum velocities and 22% for the minimum, and the absolute errors were less than 0.2m/sec. Also it was found that the average R.M.S. error between the velocities of observed and simulated was 0.119 m/sec and the average correlation coefficient was 0.70 showing close agreement. Another comparison test was done to show the result that R.M.S. error between the simulated and the observed tidal elevations at the 4 stations was 0.476m in average and the correlation coefficients were ranged 0.96~0.99. Though the simulated tidal circulation pattern in the region was well agreed with the observed, the simulated tidal velocities and elevations for specific points showed some errors with the observed. It was thought that the errors mainly due to the characteristics of TIDE Model which was developed to solve only with the linearized scheme. Finally it was concluded that, to improve the simulation results by the model, a new attempt to develop a fully nonlinear model as well as further calibration and the more reasonable generation of finite element grid would be needed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.285-289
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• 1997

A Held experiment was carried out to confirm the effect of underwater sound on the luring of fish school of rudder fish in a set net at the coast of Cheju Island. The effects of the acoustic emission on the luring of fish school were observed at a cage around a set net fishing ground using a manufactured underwater speaker. Underwater sounds that were emitted for the luring of fish school were the pure sounds of which frequency were 300Hz and 400Hz, engine noise and swimming sound. The results of the observation are as follows : 1. The input and output wave forms of a manufactured underwater speaker in water tank were similar to those in measurement frequencies. The result of the observation indicated that it could be used for the purpose of the sound emission in measurement frequencies. 2. The effect of the emitted pure sound of 300Hz, 400Hz was remarkable for the luring of fish school in 2 minutes after the sound emission. The reaction of fish school was more sensitive to the pure sound of 400Hz than 300Hz. 3. The effect of the emitted engine noise was more remarkable than that of the pure sound for 3 minute continuously. On the feeding sound, fish formed a shoal and swimmed, but didn't gather around the underwater speaker. 4. The feeding and swimming sound spectra on rudder Hsh showed similar sound pressure distribution each other, they appeared low sound pressure in frequencies of 200-600Hz.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.57-64
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• 1995

In this study, we examine the applicability of the classifier based on an artifical neural network (ANN) for the low-frequency acoustic signal in shallow water environment. The estimations of the Doppler shift and frequency spreading effect at 220 Hz reveal the frequency variation of less than 2 Hz with time This small variation enables the ANN-based classifier to identify signals using only tonal frequency information. The ANN consists of 4 layers, and has 60 input processing elements (PEs) and 4 output PEs, respectively. When measured tonal signals in the frequency 200-250 Hz are applied to the ANN-based classifier, the classifier can identify more than 67% of the signals for instantaneous frame and more than 91% for averaged one over 5 frames.