• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.33-43
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• 2020

In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundation accurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of Korea Meteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it is necessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is to establish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts was collected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfall was performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculated by extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundation probability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage from inundation.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.327-331
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• 2020

In that this study is a subject and character of risk, emerging security covers non-military areas in addition to traditional military security: environmental security, human security, resource security, and cyber security. The rise of these risks is not only changing the phenomenon of the new expansion of security areas, but also the expansion of the number and scope of security entities and the aspect of security world politics. These risks are transnational security issues at the global level in terms of their nature and extent of the damage, as well as multi-layered ones that affect local and personal security issues at the regional and national levels. In addition to national actors, non-state actors such as international organizations, multinational corporations, and global civil society, and furthermore, technology and social systems themselves are causing risks. Therefore, to solve the new security problem, it is necessary to establish a middle-level and complex governance mechanism that is sought at the regional and global levels beyond the fragmented dimension of the occurrence of new security issues that have been overlooked in the existing frame of perception, and to predict and find ways to respond to new security paradigms that have been identified in a broader sense.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.73-99
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• 2014

Recently, big data have been studied ways to use in various fields. Big data refers to huge amounts of data that could not be addressed by conventional methods. Big data has attracted attention for improving accuracy of decision-making, forecasting in the near future, and creation of new business. In this study, it is an object to develop the utilization plan for big data in the field of technology commercialization. For this reason, we conducted study like case studies, literature review and focus group interview. We have derived the big data utilization plan based on this in the technology commercialization field. It, the data utilization plan, combines with the technology commercialization process of Jolly and it has five sub processes (Imagining, Incubating, Demonstrating, Promoting, Sustaining). In this paper, there is a significance that has emphasized the possibility for big data utilization in the technology commercialization. However, there is a limit to the general interpretation for our study. And we hope to contribute to the expansion of areas of technology commercialization information analysis through this research.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.729-741
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• 2012

Among the methods of precipitation data acquisition, a rain gauge station has a distinctive advantage of direct measurement of rainfall itself, but multiple stations should be installed in order to obtain areal precipitation data required for hydrological analysis. On the other hand, a rainfall radar may provide areal distribution of rainfall in real time though it is an indirect measurement of radar echoes on rain drops. Rainfall radars have been shown useful especially for forecasting short-term localized torrential storms that may cause catastrophic flash floods. CAPPI (Constant Altitude Plan Position Indicator), which is one of the several types of radar rainfall image data, has been provided on the Internet in real time by Korea Meteorological Administration (KMA). It is one of the most widely available rainfall data in Korea with fairly high level of confidence as it is produced with bias adjustment and quality control procedures by KMA. The objective of this study is to develop an improved way to extract quantitative rainfall data applicable to even very small watersheds from CAPPI using CIVCOM, which is a new image processing method based on a vector-based scheme proposed in this study rather than raster-based schemes proposed by other researchers. This study shows usefulness of CIVCOM through comparison of rainfall data produced by image processing methods including traditional raster-based schemes and a newly proposed vector-based one.

• Journal of Environmental Science International
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• v.19 no.5
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• pp.591-605
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• 2010

This study were to simulate major criteria air pollutants and estimate regional source-receptor relationship using air quality prediction model (TAPM ; The Air Pollution Model) in the Seoul Metropolitan area. Source-receptor relationship was estimated by contribution of each region to other regions and region itself through dividing the Seoul metropolitan area into five regions. According to administrative boundary, region I and region II were Seoul and Incheon in order. Gyeonggi was divided into three regions by directions like southern(region III), northern(IV) and eastern(V) area. Gridded emissions ($1km{\times}1km$) by Clean Air Pollicy Support System (CAPSS) of National Institute of Environmental Research (NIER) was prepared for TAPM simulation. The operational weather prediction system, Regional Data Assimilation and Prediction System (RDAPS) operated by the Korean Meteorology Administration (KMA) was used for the regional weather forecasting with 30km grid resolution. Modeling period was 5 continuous days for each season with non-precipitation. The results showed that region I was the most air-polluted area and it was 3~4 times more polluted region than other regions for $NO_2$, $SO_2$ and PM10. Contributions of $SO_2$ $NO_2$ and PM10 to region I, II and III were more than 50 percent for their own sources. However region IV and V were mostly affected by sources of region I, II and III. When emissions of all regions were assumed to reduce 10 and 20 percent separately, air pollution of each region was reduced linearly and the contributions of reduction scenario were similar to those of base case. As input emissions were reduced according to different ratio - region I 40 percent, region II and III 20 percent, region IV and V 10 percent, air pollutions of region I and III were decreased remarkably. The contributions to region I, II, III were also reduced for their own sources. However, region I, II and III affected more regions IV and V. Shortly, graded reduction of emission could be more effective to control air pollution in emission imbalanced area.

• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.34-37
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• 2019

BACKGROUND: A region can be divided into agroclimatic zones based on homogeneity in weather variables that have greatest influence on crop growth and yield. The agro-climatic zone has been used to identify yield variability and limiting factors for crop growth. This study was conducted to classify agro-climatic zones in the state of Mato Grosso in Brazil for predicting crop productivity and assessing crop suitability etc. METHODS AND RESULTS: For agro-climatic zonation, monthly mean temperature, precipitation, and solar radiation data from Global Modeling and Assimilation Office (GMAO) of National Aeronautics and Space Administration (NASA, USA) between 1980 and 2010 were collected. Altitude and vegetation fraction of Brazil from Weather Research and Forecasting (WRF) were also used to classify them. The criteria of agro-climatic classification were temperature in the hottest month ($30^{\circ}C$), annual precipitation (600 mm and 1000 mm), and altitude (200 m and 500 m). The state of Mato Gross in Brazil was divided into 9 agro-climatic zones according to these criteria by using matrix classification method. CONCLUSION: The results could be useful as information for estimating agro-meteorological characteristics and predicting crop development and crop yield in the state of Mato Grosso in Brazil.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.226-237
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• 2021

Coastal safety accidents are characterized by a high proportion of human negligence and repeated occurrences of accidents caused by the same factors. The Korea Coast Guard prepares and implements various countermeasures to prevent accidents at coastal safety accident sites. However, there is a shortage of safety facilities and safety management personnel according to the limited budget. In addition, the ability to be proactively and proactively respond is low due to the limitations of the coastal safety accident risk forecasting system, which relies on the meteorological warning of the Korea Meteorological Administration. In this study, as part of preparing the foundation for establishing a preemptive and active coastal safety management system that can manage accident-causing factors, predict and evaluate risk, and implement response and mitigation measures after an accident occurs before coastal safety accidents occur. The establishment of a risk assessment system was proposed. The main evaluation factors and indicators for risk assessment were established through the analysis of the status of coastal safety accidents. The risk assessment methodology was applied to 40 major hazardous areas designated and managed by the Korea Coast Guard.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.87-98
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• 2022

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.63-74
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• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.