• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of the Korean GEO-environmental Society
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• v.19 no.12
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• pp.15-23
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• 2018

The occurrence of soil erosions in Korea is mostly driven by flowing water which has a close relationship with rainfalls. The soil eroded by rainfalls flows into and deposits in the river and it polluted the water resources and making the rivers become difficult to be managed. Recently, the frequency of heavy rainfall events that are more than 30 mm/hr has been increasing in Korea due to the influence of climate change, which creating a favourable condition for the occurrence of soil erosion within a short time. In this study, we proposed a method to estimate the distribution of rainfall intensity and to calculate the energy produced by a single rainfall event using the cumulative distribution function that take into account of the physical characteristics of rainfall. The raindrops kinetic energy estimated by the proposed method are compared with the measured data from the previous studies and it is noticed that the raindrops kinetic energy estimated by the rainfall intensity variation is very similar to the results concluded from the previous studies. In order to develop an equation for estimating rainfall kinetic energy, rainfall particle size data measured at a rainfall intensity of 0.254~152.4 mm/hr were used. The rainfall kinetic energy estimated by applying the cumulative distribution function tended to increase in the form of a power function in the relation of rainfall intensity. Based on the equation obtained from this relationship, the rainfall kinetic energy of 1~80 mm/hr rainfall intensity was estimated to be $0.03{\sim}48.26Jm^{-2}mm^{-1}$. Based on the relationship between rainfall intensity and rainfall energy, rainfall kinetic energy equation is proposed as a power function form and it is expected that it can be used in the design of short-term operated facility such as the sizing of sedimentation basin that requires prediction of soil loss by a single rainfall event.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.4
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• pp.298-309
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• 2018

This study examined the multi-functionality of an eco-pastoral system in alpine grassland and measured its economic value. The multi-functionality can be divided into three categories: direct-use value, indirect-use value, and heritage value. Direct-use value includes both extractive (forage and livestock production) and non-extractive (recreation and tourism) functions. Indirect-use value includes the functions of water conservation, soil erosion control, atmospheric control, landscape, livestock-manure management, and forest firebreaks. The heritage value includes the function of species diversity. The results showed that the annual value for 1 hectare of the eco-pastoral system in alpine grassland's direct use was estimated to be 21,090,874 Korean won; the indirect-use value was 15,562,203 won when the landscape in grassland system, and 16,018,224 won when the landscape comprised in silvopastoral system. The value of the species diversity in heritage terms ranged from 767,273 to 1,578,845 won, depending on whether it included any endangered species. The total value of multi-functionality of the eco-pastoral system in alpine grassland was estimated to be a minimum of 37,420,350 won/ha and a maximum of 38,687,942 won/ha. The results of this study can provide useful insights for the eco-pastoral system in alpine grassland policies in Republic of Korea.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.81-93
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• 2019

Phytoplankton is one of the important primary producers providing organic matter through photosynthesis in aquatic environments. In order to determine a temporal and spatial variation in primary productivity after weir construction in the Nakdong River, we investigated carbon uptake rates using in-situ $^{13}C$ labeling experiments and identified algal communities contributing to primary productivity using HPLC-CHEMTAX analysis from October to December, 2017. The primary productivity gradually decreased from fall to early winter season ($249{\sim}933mgC\;m^{-2}d^{-1}$ in October, $64{\sim}536mgC\;m^{-2}d^{-1}$ in November and $60{\sim}274mgC\;m^{-2}d^{-1}$ in December, respectively). This is attributed to the temporally declining light intensity and the decreasing biomass and physiological activity of phytoplankton in winter. The contribution of diatoms to the phytoplankton community in the Nakdong River was approximately 63% at all the sampling sites and seasons, while the contribution of cryptophytes increased from 9% in October to 32% in November and December. The temporal changes in the primary productivity and the dominant phytoplankton species in the mid and downstream weirs of the Nakdong River was investigated for the first time, after construction of the weirs, and major environmental factors controlling the temporal variation in primary productivity and phytoplankton communities were identified in this study. We suggest that seasonal field investigations will provide further information on the major environmental factors which affect the annual variation of primary productivity and phytoplankton communities.

• Journal of the Korean GEO-environmental Society
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• v.12 no.7
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• pp.5-12
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• 2011

When choosing the support pattern of tunnel, the characteristics of rock are identified from the result of the surface geologic survey, boring, and geophysical prospecting and laboratory test. And a rock mass rating is classified and excavation method and standard support pattern are designed considering rock classification, domestic and international construction practices, numerical analysis. According to the revised design standard for tunnel, it was recommended to classify the rock mass rating for the design of tunnel into a rating based on RMR. If necessary, it proposed a flexible standard allowed applying more atomized the rock mass rating and Q-System. Also, the resonable verification of the support pattern must be accompanied because the factors affecting the structure and behavior of ground during the construction of tunnel are the main factors of uncertainty factors such as the nature of ground, ground water and the characteristics of structural materials. These days, such verification method is getting more specialized and diversified. In this study, the empirical method, numerical analysis and comparative analysis of in situ measurements were used to prove the reasonableness in the support pattern by RMR and Q-value on the Imha Dam emergency spillway.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.1-11
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• 2021

Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.47-56
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• 2022

Recently, water resources and energy policies such as integrated water management and carbon neutrality are changing rapidly. There is an opinion that the value of hydropower reservoirs related to these policies should be re-evaluated. In the past, they have contributed to flood control in addition to electricity generation, such as operating at a limited water level during the flood season, but loss of power generation is inevitable with this operation. Therefore, this study introduced the concept of resilience to the hydropower generation system to minimize the power loss. A framework for evaluating the power generation performance of them was presented by defining the maximization of electricity sales as performance. Based on the current procedure of multiple operation plan, a scenario was established and simulation was performed using HEC-5. As a result of applying to the framework, it was confirmed that the power generation performance according to each scenario was evaluated as an important factor. And it was confirmed that the performance of flood control and water use could also be evaluated.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1573-1579
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• 2022

The irrigation schedule system using early detection of crop water stress is required to maintain crop production and save water resource. However, because previous studies focused on the crop under stress dominant condition, the crop physiological properties, which can be measured by remote sensing technique, on early crop water stress condition are not well known. In this study, the canopy temperature, MERIS Terrestrial Chlorophyll Index (MTCI), and Chlorophyll/Carotenoid Index (CCI) are observed on the soybeans given the early water stress using thermal imaging camera and hyperspectral camera. The increased canopy temperature and decreased MTCI are consist with the previous studies which are for the crop of stress dominant-sign. However, the CCI was increased contrary to expectation because it may faster the reduction of carotenoid than chlorophyll in early stage. These behaviors will be useful to not only develop the irrigation system but also using the early detection of crop stress.