• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.76-83
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• 2012

Korean aviation Turbulenc Guidance (KTG) system is developed using the operational unified model (UM) of the Korea Meteorological Administration (KMA) and pilot reports (PIREPs) over East Asia. The KTG system comprised of twenty turbulence diagnostics that represent various turbulence potentials and have the best forecasting skills, which are combined into a single ensemble-averaged index, namely KTG, at upper-(above FL250) and mid-(below FL250) levels. It is found that the overall performance of the KTG is higher than those produced from the one single best index, and satisfies the minimum criteria (80% accuracy) that the system is operationally useful in aviation industry.

• Atmosphere
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• v.14 no.3
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• pp.19-28
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• 2004

In this study, two years Automatic Weather Station (AWS) data observed near the coast and islands are used to evaluate gust factors only when time averaged wind speed is higher than 5 ms. The gust factors are quite different in spatial and temporal domain according to analysis method. As the averaged time is increased, the gust factors are also increased. But the gust factors are decreased when wind speed is increased. It is because each wind speed is averaged one and a maximum wind is the greatest one for each time interval. The result from t-test is shown that all data are included within the 99% significance level. A sample standard deviation of ten minutes and one minute are 0.137~0.197, 0.067~0.142, respectively. Recently, the gust factor provided at the Korea Meteorological Administration (KMA) Homepage is calculated with one-hour averaged method. All though this method is hard to use directly for forecasting the strong wind over sea and coast, the result will be a great help to express Ocean Storm Flash in the Regional Meteorological Offices and the Meteorological Stations.

• Journal of Contemporary Eastern Asia
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• v.15 no.2
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• pp.177-190
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• 2016

One of the main objectives of this paper is to provide insight to understand the effect of natural disasters on local government finance. That is, to analyze local governments' sales tax revenues after Hurricane Ike. Three Texas cities are examined: League City, Pearland, and Sugarland. Based on data collected from the Texas Comptroller's Office and the US Census, we found local governments experience a short-term increase in sales tax revenues and a long-term decline after the hurricane strike the region. On average, a major hurricane has a two-year impact on local government economy. The findings are essential for practitioners because in order to have a prosperous recovery after natural disasters, public managers have to prepare financially for short term changes in their sales tax revenues.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• The Journal of Fisheries Business Administration
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• v.38 no.2
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• pp.41-62
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• 2007

The Fish aquacultural Industry has been developed rapidly since 1990s in Korea. The total production of fish aquaculture was 5,000ton in the beginning of 1990s, but it was an excess of 80,000ton in 2005. In the beginning of 1990s, the percentage of flatfish yield was 80% of the fish aquaculture in the respect of production. And it has been maintained 50% level in 2005. In this point of view, flatfish aquaculture played the role of leader in the development of fish aquaculture. Rapid increasing of production was not only caused to decreasing in price basically, but also it threatened the management of producer into insecure price for aquacultural flatfish. Therefore, it needs the policy for stabilizing in price, but it is difficult to choose the method because the basic study was not accomplished plentifully. This study analyzed about price structure of aquacultural flatfish. A period of analysis was from January 2000 to December 2005, and a data was used monthly data for price. The principal result of this study is substantially as follows. 1) The price of producing and consuming district is closely connected. 2) A gap between producing district price and consuming district price is decreasing recently, It seems to be correlated with outlook business of aquacultural flatfish. 3) Trend line of the price was declining until 2002, but it turned up after that. The other side, circulated fluctuation was being showed typically. 4) The circle of circulated fluctuation was growing longer, so it seems that the producer was doing a sensible productive activity to cope with changing price. As a result, government's policy needs to be turned into price policy from policy of increased production for aquacultural flatfish. It seems that the best policy is price stabilization polices. And also, government needs to invest in outlook business for aquaculture constantly.

• Journal of Agricultural Extension & Community Development
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• v.20 no.2
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• pp.511-533
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• 2013

The study used a seasonal ARIMA model to forecast the number of tourists to Kyeongju foreign in a uni-variable time series. Time series monthly data for the investigation were collected ranging from 1995 to 2010. A total of 192 observations were used for data analysis. The date showed that a big difference existed between on-season and off-season of the number of foreign tourists in Kyeongju. In the forecast multiplicative seasonal ARIMA(1,1,0) $(4,0,0)_{12}$ model was found the most appropriate model. Results show that the number of tourists was 694 thousands in 2011, 715 thousands in 2012, 725 thousands in 2013, 738 thousands in 2014, and 884 thousands in 2015. It was suggested that the grasping of the Kyeongju forecast model was very important in respect of how experts in tourism development, policy makers or planners would establish marketing strategies to allocate services in Kyeongju as a tourist destination and provide tourism facilities efficiently.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2386-2395
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• 2010

The entrepreneurship is a key success factor of industrial development in global competitive environments. but there is no entrepreneurship index/indicator which gives comprehensive advantages for monitoring and forecasting entrepreneur environments in Korea. The purpose of the study is developing Entrepreneurship Survey Index(ESI) which considering various significant entrepreneur factors. The suggested ESI in this exploratory study consists of entrepreneurship business index(EBI), entrepreneurship environment index(EEI) and entrepreneurship preparation index(EPI). The EBI is composed of overall business factors which revised from practical studies and expert reviews. The EEI is mainly retrieved Global Entrepreneurship Monitor(GEM) and partially modified by an expert advisor to identify entrepreneur environments. The EPI is developed for evaluating and confirming the capability, plan and intention of the pre-entrepreneurship. The practical survey of using the proposed ESI will enhance the power of forecasting the entrepreneurship environment changes and provide effective entrepreneurship policy making for stakeholder.

• The Journal of Fisheries Business Administration
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• v.52 no.4
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.