• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.85-92
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• 2015

Since the consumption of the livestock products increased for the past 10 years in Korea, the water use for live animals has become more important in terms of water savings. Therefore, the index connecting water use and livestock products consumption should be required for sustainable water management, and water footprint concept could be suggested as the index. The aim of this study is to estimate the water footprint for livestock products; beef cattle, swine, and broiler chicken. The water footprint for livestock products is divided into direct and indirect water. The direct water includes the drinking and servicing water, and the indirect water includes the water for the cultivation of feed crops. The water footprint of beef cattle was calculated to $17,023.1m^3/ton$, and direct water was $91.2m^3/ton$, and indirect water was $16,931.9m^3/ton$. The water footprint of swine was calculated to $4,235.8m^3/ton$, and direct water was $129.7m^3/ton$, and indirect water was $4,106.0m^3/ton$. The water footprint of broiler chicken was calculated to $2,427.7m^3/ton$, and direct water was $7.6m^3/ton$, and indirect water was $2,420.1m^3/ton$. Also, we compared the water footprint to water demand of water vision 2020 which is the main report for national water management. The water vision 2020 reported only direct water for live animal, but the water footprint includes the direct and indirect water. Therefore, the water footprint could be applied to various fields relating water and food.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.103-113
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• 2000

A decision support system DSS-WQMRA (Decision Support System-Water Quality Management in Rural Area) was developed to help regional planners for the water quality management in a rural basin. The integrated model DSS-WQMRA, written in JAVA, includes four subsystems such as a GIS, a database, water quality simulation models and a decision model. In the system, the GIS deals with landuse and the location of pollutant sources. The database manages each data and supplies input data for various water quality simulation models. the water quality simulation model is composed of the GWLF( Generalized Watershed Loading Function), PCLM(Pollutant Loading Calculation Module) and the WASP5 model. The decision model based on mixed integer programming is designed to determine optimal costs and thus allow the selection of managemental practices to meet the water quality criteria. The methodology was tested with an example application in the Bokha River Basin, Kyunggi Province in Korea. It was proved that the integrated model DSS-WQMRA could be very useful for water quality management including the non-point source pollution in rural areas.

• Korean Journal of Veterinary Research
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• v.30 no.1
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• pp.103-106
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• 1990

Effects of ammonia water on the spornlation of coccidial oocysts collected from bovine feces were studied with particular reference to the various levels of ammonia water (1% to 10%) for 30 minute conservation at room temperature. The sporulation rates showed a negative linear coorelation according to the treatment leavels of ammonia water, 85.3% at 1% level to 8.9% at 10% level. The optimum level of ammonia concentration was regarded as 5% to 10% with more than 80% of sporulation inhibition effect.

• Journal of Korean Society of Rural Planning
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• v.11 no.4 s.29
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• pp.59-65
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• 2005

Proper management of small rural watershed is important since it does affect water quality improvement of larger scale watershed. Therefore, effective small watershed management guideline including participatory program of local people is required to achieve water environment improvement. Feasibility of water quality goal, short and long-term watershed management plan and funding sources were investigated by field monitoring of Hampyungchun watershed which has characteristics of rural stream, and literature review. The relevant parties and their roles fer watershed management were identified and suggested. A hybrid model, that is mixture of government driven model and NGO model, is recommended for watershed management organization in this study.

• KCID journal
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• v.19 no.1
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• pp.30-39
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• 2012

In recent years, there have been a lot of severe flood and drought disasters and the rural environment have been worsened due to rapid industrialization and urbanization in the river basins of Korea. To prevent such disasters and to improve environment in the era of climate change, Korean Government carried out 110 projects to heighten irrigation dam in the rural area. The study has been carried out to evaluate the heightening work of the irrigation dam for the supply of reserved water and to derive optimal scheme to allocate the water resource for irrigation, domestic demand and environmental conservation as well as to contribute for the rural development in sustainable way. The study is focused on the Seoam Irrigation Dam which has been constructed in 2005 to be connected with the new Gami Reservoir which has been constructed since 2010. In addition, it was studied the contribution effect of the reservoirs for the adjacent comprehensive rural development projects which have been executed by local government. In the study, the principles and visions of sustainable development which have been derived by International Commission on Irrigation and Drainage is applied to estimate the sustainability of the irrigation dams in line with the adjacent comprehensive rural development projects. The project is estimated that the water resource in the reservoirs shall be used integratedly in cooperation with various stakeholders not only to conserve water environment but also to increase productivity of agricultural goods and ecological tour in the rural area.

• Korean Journal of Environmental Agriculture
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• v.35 no.2
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• pp.148-151
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• 2016

BACKGROUND: There are few studies on the impacts of hydrophytes on water quality, so there is a need to research the effects of death of hydrophytes on the worsening of water quality. This study aimed to monitor the effects of Trapa japonica death on reservoir water quality.METHODS AND RESULTS: T.japonica shows the life cycle that highest growth in summer and rapid death in fall decomposing their body in general. T.japonica contains comparatively large portion of nutrients and minerals. Through the field survey using Mesocosm to identify the effects of excessive population of T.japonica on water quality, the water quality of plots planted T.japonica is gradually worse compared with the control plot. And the result of Wilcoxon-test also shows that the negative effect of T.japonica on water quality with significant (p<0.05).CONCLUSION: It is necessary to control the population growth of T.japonica in order to prevention of water pollution in fall.

• KCID journal
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• v.10 no.1
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• pp.112-120
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• 2003

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.