• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.345-354
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• 2021

With the evolution of containerization and globalization of supply chains, aspects of port functions have made the transition from the sea to the inland region that forms the dry port. To explore the relationship between dry ports and regional economic development, this study uses a gravity model and forecast model to analyze 1,040 observations in 104 cities (22 dry port cities) along the Yangtze River Economic Belt (YREB) from 2008 to 2017. The model includes economic variables, logistics variables, foreign relations variables, and human capital variables. It was found that the dry port is positively correlated with trade volume. Compared with a city without a dry port, the trade volume of a city with a dry port will increase 0.099 times. It can be concluded that a dry port is crucial for the economic development of the YREB. It was also found that per capita GDP as an economic variable, road area and rail number as logistics variables, and foreign relation variables are positively correlated with trade volume, while the human capital variable has no significant effect on trade volume. In addition, governmental policy implications are addressed from the aspects of dry port and industry cluster caused by foreign investment.

• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• 대기
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• 제32권3호
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• pp.191-206
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• 2022

Assimilation trials were performed using the Korea Institute of Atmospheric Prediction Systems (KIAPS) Korea Integrated Model (KIM) semi-operational forecast system to assess the impact of ground-based Global Navigation Satellite System (GNSS) Zenith Total Delay (ZTD) on forecast. To use the optimal observation in data assimilation of KIM forecast system, in this study, the ZTD observation were pre-processed. It involves the bias correction using long term background of KIM, the quality control based on background and the thinning of ZTD data. Also, to give the effect of observation directly to data assimilation, the observation operator which include non-linear model, tangent linear model, adjoint model, and jacobian code was developed and verified. As a result, impact of ZTD observation in both analysis and forecast was neutral or slightly positive on most meteorological variables, but positive on geopotential height. In addition, ZTD observations contributed to the improvement on precipitation of KIM forecast, specially over 5 mm/day precipitation intensity.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• 한국항만경제학회지
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• 제33권1호
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• pp.75-87
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• 2017

본 연구의 목적은 시스템 다이내믹스를 활용하여 선박 연료유 가격의 중장기 예측분석을 수행하는 것이다. 연료유 가격의 정확한 예측을 위해 가격 결정에 영향을 미치는 다양한 변수들 간의 인과적 관계를 바탕으로 정량화된 모델을 구축하였다. 연료유 가격 결정에는 유가에 영향을 미치는 원유 소비와 생산, 경제변화에 영향을 미치는 GDP, 환율 등과 함께 해운물류시장의 수요와 공급에 의해 결정되는 해상운임 등 다양한 구성변수들을 기반으로 시스템 다이내믹스를 활용한 연료유 가격을 예측하고 MAPEs 등을 통한 객관성을 검증하였다. 본 연구의 분석 결과 2029년까지의 연료유 가격은 2016년 대비 소폭 상승세를 보일 것으로 예상되지만 지난 2012년과 같은 급등세는 나타나지 않을 것으로 전망되었다. 본 연구는 각종 변수들 간의 동적인 인과관계를 활용하여 연료유 가격을 예측하여 합리적 추정결과를 유도할 수 있었다는 점과 가격 결정에 영향을 미치는 다양한 변수들의 구조적 관계를 손쉽게 파악함으로써 연료유 가격 변화에 대한 종합적인 위험 관리가 가능하여 해운기업의 효율적인 선대관리를 지원하는데 가치를 가지고 있다.

• 수산경영론집
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• 제42권2호
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• pp.69-83
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• 2011

This paper estimates demand functions of oyster as Kimchi's ingredients of capital area, other areas excluding a capital area, and a whole area in Korea to forecast its demand quantities in 2011~2015. To estimate oyster demand function, this paper uses pooled data produced from Korean housewives over 30 years old in 2009 and 2010. Also, this paper adopts several econometrics methods such as Ordinary Least Squares and Feasible Generalized Least Squares. First of all, to choose appropriate variables of oyster demand functions by area, this paper carries out model's specification with joint significance test. Secondly, to remedy heteroscedasticity with pooled data, this paper attempts residual plotting between estimated squared residuals and estimated dependent variable and then, if it happens, undertakes White test to care the problem. Thirdly, to test multicollinearity between variables with pooled data, this paper checks correlations between variables by area. In this analysis, oyster demand functions of a capital area and a whole area need price of the oyster, price of the cabbage for Gimjang, and income as independent variables. The function on other areas excluding a capital area only needs price of the oyster and income as ones. In addition, the oyster demand function of a whole area needed White test to care a heteroscedasticity problem and demand functions of the other two regions did not have the problem. Thus, first model was estimated by FGLS and second two models were carried out by OLS. The results suggest that oyster demand quantities per a household as Kimchi's ingredients are going to slightly increase in a capital area and a whole area, but slightly decrease in other areas excluding a capital area in 2011~2015. Also, the results show that oyster demand quantities as kimchi's ingredients for total household targeting housewives over 30 years old are going to slightly increase in three areas in 2011~2015.

• Asia pacific journal of information systems
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• 제7권1호
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• 전기학회논문지
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• 제66권4호
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• pp.621-628
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• 2017

This paper presents an artificial neural network (ANN) based model with a back-propagation algorithm for short-term load forecasting in microgrid power systems. Owing to the significant weather factors for such purpose, relevant input variables were selected in order to improve the forecasting accuracy. As remarked above, forecasting is more complex in a microgrid because of the increased variability of disaggregated load curves. Accurate forecasting in a microgrid will depend on the variables employed and the way they are presented to the ANN. This study also shows numerically that there is a close relationship between forecast errors and the number of training patterns used, and so it is necessary to carefully select the training data to be employed with the system. Finally, this work demonstrates that the concept of load forecasting and the ANN tools employed are also applicable to the microgrid domain with very good results, showing that small errors of Mean Absolute Percentage Error (MAPE) around 3% are achievable.

• 수산해양기술연구
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• 제48권4호
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• pp.445-451
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• 2012

We developed an age-based spawner-recruit model incorporating environmental variables to forecast stock biomass and recruits of pelagic fish in this study. We applied the model to the Tsushima stock of jack mackerel, which is shared by Korea and Japan. The stock biomass of jack mackerel (Trachurus japonicus) around Korean waters ranged from 141 thousand metric tons (mt) and 728 thousand mt and recruits ranged from 27 thousand mt to 283 thousand mt. We hind-casted the stock biomass to evaluate the model performance and robustness for the period of 1987~2009. It was found that the model has been useful to forecast stock biomass and recruits for the period of the lifespan of fish species. The model is also capable of forecasting the long-term period, assuming a certain climatic regime.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.