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http://dx.doi.org/10.3745/JIPS.04.0107

A Prediction Model of the Sum of Container Based on Combined BP Neural Network and SVM  

Ding, Min-jie (School of Electronic and Computer Science, Zhejiang Wanli University)
Zhang, Shao-zhong (School of Electronic and Computer Science, Zhejiang Wanli University)
Zhong, Hai-dong (School of Logistics and E-commerce, Zhejiang Wanli University)
Wu, Yao-hui (School of Electronic and Computer Science, Zhejiang Wanli University)
Zhang, Liang-bin (School of Electronic and Computer Science, Zhejiang Wanli University)
Publication Information
Journal of Information Processing Systems / v.15, no.2, 2019 , pp. 305-319 More about this Journal
Abstract
The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.
Keywords
BP Neural Network; Grey Relational Analysis; Sum of Container Prediction; Support Vector Machine;
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1 S. Makridakis and R. L. Winkler, "Averages of forecasts: Some empirical results," Management Science, vol. 29, no. 9, pp. 987-996, 1983.   DOI
2 G. Chen and Z. Yang, "Optimizing time windows for managing export container arrivals at Chinese container terminals," Maritime Economics & Logistics, vol. 12, no. 1, pp. 111-126, 2010.   DOI
3 X. Hao and R. Suo, "The application of combination forecasting model in forecasting the total power of agricultural machinery in Heilongjiang Province," Asian Agricultural Research, vol. 7, no. 5, pp. 25-28, 2015.
4 C. C. Chou, C. W. Chu, and G. S. Liang, "A modified regression model for forecasting the volumes of Taiwan's import containers," Mathematical and Computer Modelling, vol. 47, no. 9-10, pp. 797-807, 2008.   DOI
5 H. S. Hwang, S. T. Bae, and G. S. Cho, "Container terminal demand forecasting framework using fuzzy-GMDH and neural network method," in Proceedings of the 2nd International Conference on Innovative Computing, Information and Control, Kumamoto, Japan, 2007.
6 W. Y. Peng and C. W. Chu, "A comparison of univariate methods for forecasting container throughput volumes," Mathematical and Computer Modelling, vol. 50, no. 7-8, pp. 1045-1057, 2009.   DOI
7 V. Gosasang, W. Chandraprakaikul, and S. Kiattisin, "A comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok port," The Asian Journal of Shipping and Logistics, vol. 27, no. 3, pp. 463-482, 2011.   DOI
8 F. L. Chen and T. Y. Ou, "Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store," Expert Systems with Applications, vol. 36, no. 3, pp. 7054-7063, 2009.   DOI
9 V. Gosasang, W. Chandraprakaikul, and S. Kiattisin, "An application of neural networks for forecasting container throughput at Bangkok port," in Proceedings of the World Congress on Engineering, London, UK, 2010, pp. 1-5.
10 J. W. Chan and T. K. Tong, "Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach," Materials & Design, vol. 28, no. 5, pp. 1539-1546, 2007.   DOI
11 P. S. Freitas and A. J. Rodrigues, "Model combination in neural-based forecasting," European Journal of Operational Research, vol. 173, no. 3, pp. 801-814, 2006.   DOI
12 S. Yu, K. Zhu, and F. Diao, "A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction," Applied Mathematics and Computation, vol. 195, no. 1, pp. 66-75, 2008.   DOI
13 C. Dong, X. Dong, J. Gehman, and L. Lefsrud, "Using BP neural networks to prioritize risk management approaches for China's unconventional shale gas industry," Sustainability, vol. 9, article no. 979, 2017.
14 E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, "Comparison of support vector machine and artificial neural network systems for drug/nondrug classification," Journal of Chemical Information and Computer Sciences, vol. 43, no. 6, pp. 1882-1889, 2003.   DOI
15 Z. Xiao, S. J. Ye, B. Zhong, and C. X. Sun, "BP neural network with rough set for short term load forecasting," Expert Systems with Applications, vol. 36, no. 1, pp. 273-279, 2009.   DOI
16 C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article no. 27, 2011.
17 C. I. Liu, H. Jula, and P. A. Ioannou, "Design, simulation, and evaluation of automated container terminals," IEEE Transactions on Intelligent Transportation Systems, vol. 3, no. 1, pp. 12-26, 2002.   DOI
18 P. M. Schulze and A. Prinz, "Forecasting container transshipment in Germany," Applied Economics, vol. 41, no. 22, pp. 2809-2815, 2009.   DOI
19 J. K. Kim, J. Y. Pak, Y. Wang, S. I. Park, and G. T. Yeo, "A study on forecasting container volume of port using SD and ARIMA," Journal of Navigation and Port Research, vol. 35, no. 4, pp. 343-349, 2011.   DOI
20 W. Y. Peng, "The comparison of the seasonal forecasting models: a study on the prediction of imported container volume for international container ports in Taiwan," Maritime Quarterly, vol. 25, no. 2, pp. 21-36, 2006.
21 R. Diaz, W. Talley, and M. Tulpule, "Forecasting empty container volumes," The Asian Journal of Shipping and Logistics, vol. 27, no. 2, pp. 217-236, 2011.   DOI
22 F. M. Tsai and L. J. Huang, "Using artificial neural networks to predict container flows between the major ports of Asia," International Journal of Production Research, vol. 55, no. 17, pp. 5001-5010, 2017.   DOI
23 G. Xie, S. Wang, Y. Zhao, and K. K. Lai, "Hybrid approaches based on LSSVR model for container throughput forecasting: a comparative study," Applied Soft Computing, vol. 13, no. 5, pp. 2232-2241, 2013.   DOI
24 A. Huang, Z. Zhang, X. Shi, and G. Hua, "Forecasting container throughput with big data using a partially combined framework," in Proceedings of the International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 2015, pp. 641-646.
25 Q. Wu, "Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM," Expert Systems with Applications, vol. 37, no. 1, pp. 194-201, 2010.   DOI