• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Journal of the Korean Wood Science and Technology
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• v.48 no.3
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• pp.271-282
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• 2020

Sewage treatment plants are social infrastructure of cities. The sewage distribution rate in Korea is reaching 94% based on the sewage statistics based in the year of 2017. In Korean sewage treatment plants, use of PAC (Poly Aluminum Chloride) accounts for 58%. It contains a large amount of impurities (heavy metal) according to the quality standards, however, there have been insufficient efforts to reinforce the standards or technically improve the quality, which resulted in secondary pollution problems from injecting excessive coagulant. Also, the increase in the use of chemicals is leading to the increases in the annual amount of sewage sludge generated in 2017 and the need to reuse sludge. As such, this study aims to verify the possibility of reusing sludge by evaluating the stability of heavy metals based on the injection of coagulant mixture during water treatment which uses the torrefield wood powder and natural materials, and evaluating the sedimentation and heating value of sewage sludge. As a result of analyzing heavy metals (Cr, Fe, Zn, Cu, Cd, As, Pb, and Ni) from the coagulant mixture and PAC (10%), Cr, Cd, Pb, Ni, and Hg were not detected. As for Zn, while its concentration notified in the quality standards for drinking water is 3 mg/L, only a small amount of 0.007 mg/L was detected in the coagulant mixture. Maximum amounts of over double amounts of Fe, Cu, and As were found with PAC (10%) compared to the coagulant mixture. Also, an analysis of sludge sedimentation found that the coagulant mixture showed a better performance of up to double the speed of the conventional coagulant, PAC (10%). The dry-basis lower heating value of sewage sludge produced by injecting the coagulant mixture was 3,378 kcal/kg, while that of sewage sludge generated due to PAC (10%) was 3,171 kcal/kg; although both coagulants met the requirements to be used as auxiliary fuel at thermal power plants, the coagulant mixture developed in this study could secure heating values 200 kal/kg higher than the counterpart. Therefore, utilization of the coagulant mixture for water treatment rather than PAC (10%) is expected to be more environmentally stable and effective, as it helps generating sludge with better stability against heavy metals, having a faster sludge sedimentation, and higher heating value.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.