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

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.475-476
• /
• 2015

전력수요는 다양한 외부요인으로부터 영향을 받으므로 전력수요 예측 시 각 요인과의 상관관계를 고려할 필요가 있다. 본 논문은 Stepwise 다중회귀분석법을 이용한 일일 최대전력수요 예측 방법을 제시하였다. 사례연구에서는 2014년 평일 전력수요데이터를 이용하여 제안된 예측방법을 적용하고 그 결과를 평가하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.1B
• /
• pp.39-50
• /
• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.1513-1514
• /
• 2013

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.3
• /
• pp.513-521
• /
• 2010

Most of universities have carried out course evaluation to apply the performance appraisal for professor. But, course evaluation depends on characteristics of each class such as class size, type of lecture, evaluator's grade and so on. As the results, such characteristics of each class lead to serious bias which makes lecturers distrust the course evaluation results. Hence, we propose a equating method for the course evaluation by regression analysis which use stepwise variable selection. And we compare proposed method with the other method by Cho et al. (2009) with respect to efficiencies. Also we give the example to which the method is applied.

• Journal of KIISE:Software and Applications
• /
• v.41 no.8
• /
• pp.598-604
• /
• 2014

In this paper, we develop stepwise regression data envelopment model to select important variables. We formulate null hypothesis to understand the importance of each variable and use Kruskal-Wallis test for this purpose. If the Kruskal-Wallis test does reject the null hypothesis this will imply there is significant fluctuation in the efficiency score relative to base model. And therefore we have to further check the pair of variables that causes the fluctuation in order to determine its importance using Conover-Inman test. The proposed models helps understand the extent of misclassification decision making units as efficient/inefficient when variables are retained or discarded alongside provides useful managerial prescription to make improvement strategies.

• Journal of Navigation and Port Research
• /
• v.42 no.6
• /
• pp.539-545
• /
• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Korean Society of Forest Science
• /
• v.82 no.4
• /
• pp.420-430
• /
• 1993

This study was conducted to investigate the effect of the forest environmental factors(5 items) and physico-chemical properties of soil(13 items) on the growth of Pinus thunbergii stands. The 218 plots were sampled over the coastal district of the whole country. In statistical analysis, the explanatory variables were soil and environmental factors(18 items), and the response variable was the site index of Pinus thunbergii stands. Data computation was processed in order of preparation of original data, computation of inner correlation matrix table by correlation analysis, calculation of partial correlation coefficients and coefficients of determination, estimation of regression equation by stepwise begression analysis, and stepwise regression analysis by factor score of factor analysis. The main results obtained were summarized as follows ; 1. The site index in Pinus thunbergii stands way highly correlated with effective soil depth(r=0.8668), slope percentage, organic matter, and total nitrogen. 2. According to the coefficients by partial correlation analysis, effective soil depth(r=0.6270), slope percentage (r=-0.5423) and base saturation(r=0.3278) among environmental factors had a great effect on tree growth. 3. With stepwise regression analysis, the factors effecting on the Pinus thunbergii stands growth were effective soil depth, slope percentage, organic matter, base saturation, soil pH, content of silt, exchangeable Ca, and etc. 4. Estimation equation for the site index of Pinus thunbergii stands was given by $Y=13.2691+0.0242\;X_2-1.2244\;X_4+0.6142\;X_5-0.3472\;X_{11}+0.0355\;X_{13}+0.1552\;X_{15}-0.1002\;X_{17}$. The coefficient of determination for the estimation model was 0.77, which was significant at the 1 percent level. 5. In result of factor analysis by the environmental factors, principal components were 6 factors, and communality contribution percentage was 71.1 percent. 6. By stepwise regression analysis between factor score and site index of Pinus thunbergii stands, the factor group effecting on site index was 5 principal components. The coefficients of determination was 85 percent, which was significant at the 1 percent level. In conclusion, on the occasion of analizing which factors to effect on the tree height growth in Pinus thunbergii stands the stepwise regression analysis proved to be greatly significant. Also the management of Pinus thunbergii stands should be working by the above selected growth factors.

• Spatial Information Research
• /
• v.18 no.5
• /
• pp.107-117
• /
• 2010

Taking the East-Hwasung area as the case, this study first builds gridded population data based on the municipal population survey raw data, and then measures, by way of GIS tools, the major urban spatial variables that are thought to influence the composition of the regional population. For the purpose of comparison, the urban models based on the Genetic Algorithm technique and the regression technique are constructed using the same input variables. The findings indicate that the GA output performed better in differentiating the effective variables among the pilot model variables, and predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression models. The study results indicate that GA technique could be a very useful and supplementary research tool in understanding the urban phenomena.

• Journal of Digital Contents Society
• /
• v.19 no.1
• /
• pp.141-147
• /
• 2018

This study analyzed the early retired employees who stayed in company no longer than 3 years based on a certain company's personality evaluation result data. The predicted model was analyzed by dividing into two categories; the manufacture group and the R&D group. Independent variables were selected according to the stepwise method. A logistic regression model was selected as a prediction model among various supervised learning methods, and trained through cross-validation to prevent over-fitting or under-fitting. The accuracy of the two groups were confirmed by the confusion matrix. The most influential factor for early retirement in the manufacture group was revealed as "immersion," and for the R&D group appeared as "antisocial." In the past, people concentrated on collecting data by questionnaire and identifying factors that are highly related to the retirement, but this study suggests a sustainable early retirement prediction model in the future by analyzing the tangible outcome of the recruitment process.