여객수와 화물량에 대한 예측은 터미널의 개발 및 계획, 선사의 적정선복량 화보를 위해 중요하다. 본 연구에서는 역전파 학습 알고리즘을 이용한 뉴럴네트웍을 이용하여 목포항 여객수와 화물량을 예측하였다. 그리고 이동평균법, 지수평활법, 뉴럴네트웍의 예측수행을 평균제곱오차, 절대평균오차로 비교하여 뉴럴네트웍의 예측수행능력이 우수함을 검정하였다. 또한 2005년 목포항 여객수와 화물량을 예측하여 여객선 선복량의 적정성을 분석하였다.

The aim of this paper is to forecast passenger numbers and freight volumes in 2005 and it is proposed optimal tonnage of passenger ship. The forecasting of passenger numbers and freight volumes is important problem in order to determine optimal tonnage of passenger ship, port plan and development. In this paper, the forecasting of passenger numbers and freight volumes are performed by the method of neural network using back-propagation learning algorithm. And this paper compares the forecasting performance of neural networks with moving average method and exponential smooth method As the result of analysis. The forecasting of passenger numbers and freight volumes is that the neural networks performed better than moving average method and exponential smoothing method on the basis of MSE(mean square error) and MAE(mean absolute error).

• 장운재 | Jang, Woon-Jae
• 금종수 | Keum, Jong-Soo