• Journal of environmental and Sanitary engineering
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• v.24 no.3
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• pp.37-54
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• 2009

The study has analyzed impacts and intensity of weather that affect $PM_{10}$ concentration based on PM10 forecast conducted by the city of Seoul in order to identify ways to improve the accuracy of PM10 forecast. Variables that influence $PM_{10}$ concentration include not only velocity and direction of the wind and rainfalls, but also those including secondary particulate matter, which were identified to greatly influence the concentration in complicated manner as well. In addition, same variables were found to have different impacts depending on seasons and conditions of other variables. The study found out that improving accuracy of $PM_{10}$ concentration forecast face some limits as it is greatly influenced by the weather. As an estimation, this study assumed that basic research units and artificially estimated pollutant emissions, study on mechanisms of secondary particulate matter productions, observatory compliment, and enhanced forecaster's expertise are needed for better forecast.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.3
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• pp.57-62
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• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.237-247
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• 2022

This study predicts the economic activity level of the elderly in Korea using various machine learning methods. While the previous studies mainly focused on testing the relationship between the economic activity level and the life satisfaction or the social security system, this study aims at the accurate prediction on the economic activity level of the elderly using various machine learning methods and the forecast combination. Dependent variables such as the activity rate, employment rate, etc and independent variables such as the income, average wage, etc compose the dataset in this study. Five different machine learning methods and two forecast combinations are applied to the given dataset. The prediction performances of the machine learning method and the forecast combination varied across the dependent variables and prediction intervals, but it was found that the forecast combination was relatively superior to other methods in terms of the stability of prediction. This study has significance in that it accurately predicted the economic activity level of the elderly and achieved the stability of the prediction, raising practicality from a policy perspective.

• Atmosphere
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• v.32 no.2
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Journal of Korean Academy of Nursing
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• v.34 no.6
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• pp.954-963
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• 2004

Purpose: This study was designed to identify the variables affecting male adolescents' sexual intercourse through a comprehensive analysis of individual and environmental factors. Method: The subjects of this descriptive survey on causal relations were 462 subjects enrolled in liberal and vocational high schools selected on a convenience sampling basis. The data collected from May-July 2002 was put to logistic regression analysis to build a forecast model. Findings: 1) Individual factors such as school record, experience seeking, non-inhibition and sexual permissiveness, 2) family factors such as parental living arrangement, 3) school factors such as career tract and 4) peer factors such as having a boy/girl friend were identified as significant variables forecasting sexual intercourse. Conclusion and Recommendation: The theoretical model built on the basis of the major findings of this study will hopefully help promote a wholesome youth culture related to sexual intercourse. It is recommended that a program be developed that can help control the variables identified in this study along with a follow-up study to verify the model.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.1
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• pp.43-50
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• 2019

The aviation demand forecast field has been actively studied along with the recent growth of the aviation market. In this study, the demand for domestic passenger demand and freight demand was estimated through cross-validation method. As a result, passenger demand is influenced by private consumption growth rate, oil price, and exchange rate. Freight demand is affected by GDP per capita, private consumption growth rate, and oil price. In particular, passenger demand is characterized by temporary external shocks, and freight demand is more affected by economic variables than temporary shocks.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.49-57
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• 2020

This study predicts the ratio of added value, which represents the competitiveness of export industries in South Korea, using various machine learning techniques. To enhance the accuracy and stability of prediction, forecast combination technique was applied to predicted values of machine learning techniques. In particular, this study improved the efficiency of the prediction process by selecting key variables out of many variables using recursive feature elimination method and applying them to machine learning techniques. As a result, it was found that the predicted value by the forecast combination method was closer to the actual value than the predicted values of the machine learning techniques. In addition, the forecast combination method showed stable prediction results unlike volatile predicted values by machine learning techniques.

• The Journal of Society for e-Business Studies
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• v.6 no.3
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• pp.197-207
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• 2001

The industrial structure comes to be complicated and for the production of the enterprise the rational and scientific forecast is necessary. The demand forecast has been widely used to linear regression, and up to now the linear regression was sharp the relationskp between then dependent variable and the independent variables. But, The real society demands accurate demand forecast from uncertain environment and subjective concept. This paper proposes the demand quantity forecast method to using of the fuzzy linear regression in uncertain and vague environment. Also, the optimum decision making of the demand quantity forecast uses integral calculus of the Sugeno to reflecting with the expert's (inventory manager) opinion.