• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.28-34
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• 2020

A reservoir is defined as an artificial facility that stores and controls water during floods and droughts. Korea has constructed and managed reservoirs all over the country to benefit farming communities. The importance of reservoirs has decreased gradually due to urbanization and the spread of tap water, but the importance of water is increasing because of the recent shortage of water and the resulting rise in the price of water resources. Therefore, this study suggests countermeasures through an analysis of the used threshold for agricultural reservoirs. To this end, the forecast of rainfall up to 2100 was first analyzed using flood estimates and RCP scenarios through rainwater data collection. The increase in the RCP 8.5 scenario, the largest increase in the probability rainfall, was calculated by adding it to the current probability rainfall, and it was predicted that the marginal height of Odong Dam would reach its limit in 2028. Therefore, as a countermeasure against this, the measures to secure effective water storage were suggested through measures, such as lowering the height of Yeosu and installing movable beams. Overall, it is expected that effective management of the reservoirs used for agriculture will be possible in the future.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Journal of Food Hygiene and Safety
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• v.27 no.4
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• pp.339-348
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• 2012

The purpose of this study is to predict the economic size of foods-for-elderly market, which will be valuable information for establishing related policy and backup system. After setting the scope of related industry, detailed information for current market situation was investigated and a systematic forecast for market changes in the future was performed. Economic growth, changes in consumer expenditure and economic status of the elderly, current subscription of medical insurance and saving for pension were reflected. In addition, a survey toward related firms was completed and changes in aged population and incidence of chronic disease in the elderly were taken into account. Results show that the annual growth rate of the market was predicted to be the minimum 4.54% through the maximum 8.32% from 2010 to 2025 and its market size was forecasted to be the minimum 7,073 ten million won through the maximum 10,976 ten million won. It is expected that the market of foods-for-elderly will grow rapidly with development of foods technology and fast increase of aged population. Especially, growth of health functional foods and foods for special dietary uses for elderly will be distinguished. However, it seems that related firms are on the hedge, watching current trend of the related industry. This may results in insufficient supply against the demand. Therefore, policy for foods-for-elderly should be introduced and systematically administered, including R&D support, standardization and authentication for foods-for-elderly, construction of related database system.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.153-163
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• 2014

This study analyzed directions of the energy product efficiency improvement and Carbon Tax for the domestic building sector. In order to analyze GHG reduction potential and total cost, the cost optimization model MESSAGE was used. In the case of the "efficiency improvement scenario," the cumulative potential GHG reduction amount - with respect to the "Reference scenario" - from 2010 to 2030 is forecast to be $104MtCO_2eq$, with a total projected cost of 2.706 trillion KRW. In the "carbon tax scenario," a reduction effect of $74MtCO_2eq$ in cumulative potential GHG reduction occurred, with a total projected cost of 2.776 trillion KRW. The range of per-ton GHG reduction cost for each scenario was seen to be approximately $-475{\sim}272won/tCO_2eq$, and the "efficiency improvement scenario" showed as the highest in the order of priority, in terms of the GHG reduction policy direction. Regarding policies to reduce GHG emissions in the building sector, the energy efficiency improvement is deemed to deployed first in the future.

• International Area Studies Review
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• v.13 no.3
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• pp.331-359
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• 2009

We forecast the performance of the Korean biotechnology industry by adopting similar development paths taken by the U.S. biotechnology and Korean ICT industries. Our long-term forecasting techniques predict that Korean BT market size will increase from 3.7 billion to 10.8 billion U.S. dollars by year 2030. The pharmaceutical industry, one of major bio-subindustries, is expected to dominate Korean BT market in the long-run. Also, the relative portion of the exports in the Korean BT industry will be larger and thus the export-oriented government policy is required for the long-run growth of the Korean BT industry. Since the Korean ICT industry has already slowed down in the development, Korean BT industry is likely to catch up with ICT industry in the near future.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.1
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• pp.85-91
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• 2021

In contrast to standard train tracks, tramlines are often set along public roads, with trams running among pedestrians and other vehicles. In some cities and towns, trams and buses share the same routes and stations. Under the current investment appraisal system, trams are classified into light rail when predicting traffic demand and calculating benefits, but in the case of non-capital areas, it is notable that the origin-destination and transit lines of buses are not provided in the Korea Transport Database distribution data. Due to this problem, it is difficult to reflect proper mode changing behaviors between route buses and trams. This study examines the impact on tramlines of bus routes that are not currently considered in non-capital areas. Following an analysis of the effect of tram projects according to whether bus routes are considered or not, an improvement in methodology is proposed. Through this study, it is expected that the investment appraisal system for the planning of new tramlines will be improved in the future.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.214-223
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• 2022

The purpose of this study is to present a new industrial land demand prediction method that can consider external economic factors. The analysis model used ARIMA-X, which can consider exogenous variables. Exogenous variables are composed of macroeconomic variable, Business Survey Index, and Composite Economic Index variables to reflect the economic and industrial structure. And, among the exogenous variables, only variables that precede the supply of industrial land are used for prediction. Variables with precedence in the supply of industrial land were found to be import, private and government consumption expenditure, total capital formation, economic sentiment index, producer's shipment index, machinery for domestic demand and composite leading index. As a result of estimating the ARIMA-X model using these variables, the ARIMA-X(1,1,0) model including only the import was found to be statistically significant. The industrial land demand forecast predicted the industrial land from 2021 to 2030 by reflecting the scenario of change in import. As a result, the future demand for industrial land was predicted to increase by 1.91% annually to 1,030.79 km². As a result of comparing these results with the existing exponential smoothing method, the results of this study were found to be more suitable than the existing models. It is expected to b available as a new industrial land forecasting model.