MODELING OF GENERAL CARGO SHIP’S MAIN ENGINE POWERS WITH REGRESSION BASED MACHINE LEARNING ALGORITHMS: COMPARATIVE RESEARCH

Abstract

This study, which allows estimating main engine power of new ships based on data from general cargo ships, consists of a series of mathematical relationships. Thanks to these mathematical relationships, it can be predicted main engine power according to length (L), gross tonnage (GT) and age of a general cargo ship. In this study, polynomial regression, K-Nearest Neighbors (KNN) regression and Gradient Boosting Machine (GBM) regression algorithms are used. By this means the relationships presented here, it is aimed to build ships that are environmentally friendly and can be sustained at a lower cost by using the main engine power of the new ships with high accuracy. In addition, the relationships presented here provide validation for computational fluid dynamics (CFDs) and other studies with empirical statements. As a result of the study, polynomial regression gives similar results with other studies in the literature. We also concluded that while KNN regression yields fast results, GBM regression algorithm provides more accurate solutions to estimate the ship's main engine power.

Keywords

• Machine learning
• Regression algorithm
• General cargo ship
• Engine power
• Prediction

References

1. Cepowski, T. (2019). "Regression Formulas for The Estimation of Engine Total Power for Tankers, Container Ships and Bulk Carriers on The Basis of Cargo Capacity and Design Speed" Polish Maritime Research, 26 (1): 82–94. https://doi.org/10.2478/pomr-2019-0010.
2. Chomboon, K., Chujai, P., Teerarassammee, P., Kerdprasop, K. and Kerdprasop, N. (2015). "An Empirical Study of Distance Metrics for K-Nearest Neighbor Algorithm" The 3rd International Conference on Industrial Application Engineering 2015 (ICIAE2015) Japan, pp. 280–85. https://doi.org/10.12792/iciae2015.051.
3. Ekinci, S., Celebi, U.B., Bal, M., Amasyali, M.F. and Boyaci, U.K. (2011). "Predictions of Oil/Chemical Tanker Main Design Parameters Using Computational Intelligence Techniques" Applied Soft Computing, The Impact of Soft Computing for the Progress of Artificial Intelligence, 11 (2): 2356–66. https://doi.org/10.1016/j.asoc.2010.08.015.
4. Gheibi, O., Weyns,D. and Quin, F. (2021). “Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review” ArXiv:2103.04112 [Cs], March. http://arxiv.org/abs/2103.04112.
5. Gkerekos, C., Lazakis, I. and Theotokatos, G. (2019). “Machine Learning Models for Predicting Ship Main Engine Fuel Oil Consumption: A Comparative Study” Ocean Engineering, 188 (September): 106282. https://doi.org/10.1016/j.oceaneng.2019.106282.
6. Jeon, M., Noh, Y., Shin, Y., Lim, O-K. Lee, I. and Cho, D. (2018). “Prediction of Ship Fuel Consumption by Using an Artificial Neural Network” Journal of Mechanical Science and Technology 32 (12): 5785–96. https://doi.org/10.1007/s12206-018-1126-4.
7. Kaluza, P., Kölzsch, A., Gastner, M.T. and Blasius, B. (2010). “The Complex Network of Global Cargo Ship Movements” Journal of The Royal Society Interface, 7 (48): 1093–1103. https://doi.org/10.1098/rsif.2009.0495.
8. Li, X., Sun, B., Guo, C., Du, W. and Li, Y. (2020). “Speed Optimization of a Container Ship on a given Route Considering Voluntary Speed Loss and Emissions” Applied Ocean Research, 94 (January): 101995. https://doi.org/10.1016/j.apor.2019.101995.

9. Peng Y., Liu, H., Li, X., Huang, J. and Wang, W. (2020). “Machine Learning Method for Energy Consumption Prediction of Ships in Port Considering Green Ports” Journal of Cleaner Production, 264: 121564. https://doi.org/10.1016/j.jclepro.2020.121564.
10. Requia, W.J., Coull, B.A., Koutrakis, P. (2019). “Evaluation of Predictive Capabilities of Ordinary Geostatistical Interpolation, Hybrid Interpolation, and Machine Learning Methods for Estimating PM2.5 Constituents over Space” Environmental Research, 175, pp. 421–33. https://doi.org/10.1016/j.envres.2019.05.025.
11. Stopford, M. (2008). Maritime Economics, 3e (3rd ed.) Routledge. https://doi.org/10.4324/9780203891742.
12. Trozzi, C. (2010). “Emission Estimate Methodology for Maritime Navigation” Co-Leader of the Combustion & Industry Expert Panel.
13. Uyanık, T., Karatuğ, Ç. and Arslanoğlu, Y. (2020). “Machine Learning Approach to Ship Fuel Consumption: A Case of Container Vessel” Transportation Research Part D: Transport and Environment, 84 (July): 102389. https://doi.org/10.1016/j.trd.2020.102389.
14. Yan R., Wang S., Du Y. (2020). “Development of a Two-Stage Ship Fuel Consumption Prediction and Reduction Model for a Dry Bulk Ship” Transportation Research Part E: Logistics and Transportation Review, 138 (July 2019): 101930. https://doi.org/10.1016/j.tre.2020.101930.