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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(2): 139-147
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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