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WANG Sheng-zheng, SHEN Xin-quan, ZHAO Jian-sen, JI Bao-xian, YANG Ping-an. Prediction of marine meteorological effect on ship speed based on ASAE deep learning[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 139-147. doi: 10.19818/j.cnki.1671-1637.2018.02.015
Citation: WANG Sheng-zheng, SHEN Xin-quan, ZHAO Jian-sen, JI Bao-xian, YANG Ping-an. Prediction of marine meteorological effect on ship speed based on ASAE deep learning[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 139-147. doi: 10.19818/j.cnki.1671-1637.2018.02.015
shu

Prediction of marine meteorological effect on ship speed based on ASAE deep learning

doi: 10.19818/j.cnki.1671-1637.2018.02.015

  • Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

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  • Author Bio:

    WANG Sheng-zheng(1976-), male, professor, PhD, szwang@shmtu.edu.cn

  • Received Date: 2017-11-02
  • Publish Date: 2018-04-25
    Abstract
  • In order to effectively predict the marine meteorological effect on ship speed, the alternating sparse auto-encoders (ASAE) network model based on the sparse auto-encoders (SAE) network model was proposed.A framework for predicting the marine meteorological effect on ship speed was constructed, and the association rules method was proposed to feature the navigation data, so as to excavate the factors and implicit relations affecting ship speed.Through the integration of ship navigation data provided by China COSCO Shipping Group and the meteorological data provided by the National Oceanic and Atmospheric Administration, ASAE network model was trained with training samples and verified with test samples, and theprediction result was compared to the results gained by support vector regression (SVR) model, back propagation neural network (BPNN) model, deep belief network (DBN) model and SAE network model.Research result shows that the training time and the mean squared error of marine meteorological effect on ship speed gained by ASAE network model are 8.2 sand 0.287 3 kn, respectively.Compared to SVR model, BPNN model, DBN model, and SAE network model, ASAE network model can shorten the training time by 1 683.1, 66.9, 2.0 and 1.5 s, respectively, and can increases the prediction accuracy by 0.045 5, 0.296 9, 0.153 4 and 0.178 6 kn.The forecast result of ASAE network model is more in line with actual sea condition, and can dynamically master the marine meteorological effect on ship speed.Estimating the actual speed through the predicted values can optimize the ship transportation process in meteorological navigation.It plays an auxiliary role to accurately consider the complex impacts of marine meteorological on navigation optimization strategies such as route planning and speed recommendation.Thereby, it improves the energy efficiency indicator of ship operation and achieves the purpose of energy saving, low-carbon and green navigation.

     

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    • References(23)
  • [1]
    SUN Jian, WANG Feng-wu, LIU Qiang, et al. Safety assessment of ships navigating in heavy sea based on evidence theory[J]. Journal of Dalian Maritime University, 2013, 39 (1): 53-56. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201301013.htm
    [2]
    YU Shan-shan, WANG Chuan-xu. Speed optimization under different carbon emission control policy[J]. Journal of Dalian Maritime University, 2015, 41 (3): 45-50. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201503009.htm
    [3]
    MENG Xiao-dong, YUAN Zhang-xin. A minimum fuel consumption speed model considering effect of irregular wind and wave[J]. Journal of Shanghai Maritime University, 2016, 37 (1): 19-24. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SHHY201601004.htm
    [4]
    SHENG X M, CHEW E P, LEE L H. (s, S) policy model for liner shipping refueling and sailing speed optimization problem[J]. Transportation Research Part E: Logistics and Transportation Review, 2015, 76: 76-92. doi: 10.1016/j.tre.2014.12.001
    [5]
    KOBAYASHI E, HASHIMOTO H, TANIGUCHI Y, et al. Advanced optimized weather routing for an ocean-going vessel[C]//IEEE. Proceedings of 2015 International Association of Institutes of Navigation World Congress. New York: IEEE, 2015: 1-8.
    [6]
    LI Yuan-kui, ZHANG Ying-jun, GAO Zong-jiang, et al. Optimal routing model for wind-assisted ship[C]//IEEE. Proceedings of 2013International Conference on Mechatronic Sciences, Electric Engineering and Computer. New York: IEEE, 2013: 1567-1571.
    [7]
    LU R H, TURAN O, BOULOUGOURIS E, et al. A semiempirical ship operational performance prediction model for voyage optimization towards energy efficient shipping[J]. Ocean Engineering, 2015, 110: 18-28. doi: 10.1016/j.oceaneng.2015.07.042
    [8]
    VENETI A, KONSTANTOPOULOS C, PANTZIOU G. An evolutionary approach to multi-objective ship weather routing[C]//IEEE. Proceedings of the 6th International Conference on Information, Intelligence, Systems and Applications. New York: IEEE, 2015: 1-6.
    [9]
    SHI Bu-hai, GUO Xie-tao, ZHANG Ben. Research for economy shipping of oceangoing vessel based on the FOA-SVR[C]//IEEE. Proceedings of the 32nd Chinese Control Conference. New York: IEEE, 2013: 7563-7568.
    [10]
    LI Gang, XU Huan, LIU Wei. Option of operating speed for vessels under low-carbon economy[J]. Journal of Industrial Engineering and Management, 2013, 6 (1): 289-296.
    [11]
    LI Xiao-ming, XIAO Jian-mei, WANG Xi-huai. Optimization of ship routing with tabu search algorithm[C]//IEEE. Proceedings of 2011International Conference on Energy and Environment. New York: IEEE, 2011: 139-142.
    [12]
    CHEN Yuan-chao, WU Qi-rui, WANG Yu-cheng. A research on precise prediction method of high-speed craft resistance based on CFD[C]//IEEE. Proceedings of the 8th International Conference on Intelligent Computation Technology and Automation. New York: IEEE, 2015: 84-88.
    [13]
    YANG H F, DILLON T S, CHEN Y P P. Optimized structure of the traffic flow forecasting model with a deep learning approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28 (10): 1-11.
    [14]
    GAN Shao-jun, LIANG Shan, LI Kang, et al. Long-term ship speed prediction for intelligent traffic signaling[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18 (1): 1-10.
    [15]
    SALMAN A G, KANIGORO B, HERYADI Y. Weather forecasting using deep learning techniques[C]//IEEE. Proceedings of 2015 International Conference on Advance Computer Science and Information Systems. New York: IEEE, 2015: 281-285.
    [16]
    KOESDWIADY A, SOUA R, KARRAY F. Improving traffic flow prediction with weather information in connected cars: a deep learning approach[J]. IEEE Transaction on Vehicular Technology, 2016, 65 (12): 1-10.
    [17]
    HOSSAIN M, REKABDAR B, LOUIS S J, et al. Forecasting the weather of Nevada: a deep learning approach[C]//IEEE. Proceedings of 2015International Joint Conference on Neural Networks. New York: IEEE, 2015: 1-6.
    [18]
    BENGIO Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2 (1): 1-12.
    [19]
    JIANG Xiao-juan, ZHANG Yin-hua, ZHANG Wen-shen, et al. A novel sparse auto-encoder for deep unsupervised learning[C]//IEEE. Proceedings of 6th International Conference on Advanced Computational Intelligence. New York: IEEE, 2013: 256-261.
    [20]
    BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks[J]. Advances in Neural Information Processing Systems, 2007, 19 (3): 153-160.
    [21]
    FRIGUI H, NASRAOUI O. Unsupervised learning of prototypes and attribute weights[J]. Pattern Recognition, 2004, 37 (3): 567-581.
    [22]
    KARANCE L, ERDEM A, ERDEM E. Image matting with KL-divergence based sparse sampling[C]//IEEE. Proceedings of2015IEEE International Conference on Computer Vision. New York: IEEE, 2015: 7-13.
    [23]
    CAI C H, FU A W C, CHENG C H, et al. Mining association rules with weighted items[C]//IEEE. International Symposium on Database Engineering and Applications. New York: IEEE, 1998: 1-10.
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