Have a personal or library account? Click to login
Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms Cover

Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms

Open Access
|Apr 2021

References

  1. 1. A. Ekmekçioğlu, K. Ünlügençoğlu, and U. B. Çelebi, ‘Ship emission estimation for Izmir and Mersin international ports – Turkey’, Journal of Thermal Engineering, vol. 5, no. 6, pp. 184–195, 2019, doi: 10.18186/thermal.654319.10.18186/thermal.654319
  2. 2. C. Trozzi, ‘Emission estimate methodology for maritime navigation’, Co-leader of the Combustion & Industry Expert Panel, 2010.
  3. 3. R. Yan, S. Wang, and Y. Du, ‘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, vol. 138, no. July 2019, p. 101930, 2020, doi: 10.1016/j.tre.2020.101930.10.1016/j.tre.2020.101930
  4. 4. L. Huang, Y. Wen, Y. Zhang, C. Zhou, F. Zhang, and T. Yang, ‘Dynamic calculation of ship exhaust emissions based on real-time AIS data’, Transportation Research Part D: Transport and Environment, vol. 80, no. August 2019, p. 102277, 2020, doi: 10.1016/j.trd.2020.102277.10.1016/j.trd.2020.102277
  5. 5. T. A. Tran, ‘Effect of ship loading on marine diesel engine fuel consumption for bulk carriers based on the fuzzy clustering method’, Ocean Engineering, vol. 207, no. January 2019, p. 107383, 2020, doi: 10.1016/j.oceaneng.2020.107383.10.1016/j.oceaneng.2020.107383
  6. 6. X. Yan, K. Wang, Y. Yuan, X. Jiang, and R. R. Negenborn, ‘Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors’, Ocean Engineering, vol. 169, no. August, pp. 457–468, 2018, doi: 10.1016/j. oceaneng.2018.08.050.
  7. 7. T. Cepowski, ‘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, vol. 26, no. 1, pp. 82–94, Mar. 2019, doi: 10.2478/pomr-2019-0010.10.2478/pomr-2019-0010
  8. 8. W. J. Requia, B. A. Coull, and P. Koutrakis, ‘Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space’, Environmental Research, vol. 175, no. April, pp. 421–433, 2019, doi: 10.1016/j.envres.2019.05.025.10.1016/j.envres.2019.05.02531154232
  9. 9. T. Uyanık, Ç. Karatuğ, and Y. Arslanoğlu, ‘Machine learning approach to ship fuel consumption: A case of container vessel’, Transportation Research Part D: Transport and Environment, vol. 84, 2020, doi: 10.1016/j.trd.2020.102389.10.1016/j.trd.2020.102389
  10. 10. L. Barua, B. Zou, and Y. Zhou, ‘Machine learning for international freight transportation management: A comprehensive review’, Research in Transportation Business and Management, no. July 2019, p. 100453, 2020, doi: 10.1016/j.rtbm.2020.100453.10.1016/j.rtbm.2020.100453
  11. 11. Y. Peng, H. Liu, X. Li, J. Huang, and W. Wang, ‘Machine learning method for energy consumption prediction of ships in port considering green ports’, Journal of Cleaner Production, vol. 264, p. 121564, 2020, doi: 10.1016/j. jclepro.2020.121564.
  12. 12. J. H. Jeong, J. H. Woo, and J. G. Park, ‘Machine learning methodology for management of shipbuilding master data’, International Journal of Naval Architecture and Ocean Engineering, vol. 12, pp. 428–439, 2020, doi: 10.1016/j. ijnaoe.2020.03.005.
  13. 13. C. Gkerekos, I. Lazakis, and G. Theotokatos, ‘Machine learning models for predicting ship main engine fuel oil consumption: A comparative study’, Ocean Engineering, vol. 188, no. August, p. 106282, 2019, doi: 10.1016/j. oceaneng.2019.106282.
  14. 14. A. Jonquais and F. Krempl, ‘Predicting Shipping Time with Machine Learning’, 2019.
  15. 15. O. Bodunov, F. Schmidt, A. Martin, A. Brito, and C. Fetzer, ‘Grand challenge: Real-time destination and ETA prediction for maritime traffic’, DEBS 2018 – Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems, pp. 198–201, 2018, doi: 10.1145/3210284.3220502.10.1145/3210284.3220502
  16. 16. J. Yuan and V. Nian, ‘Ship energy consumption prediction with Gaussian process metamodel’, Energy Procedia, vol. 152, pp. 655–660, 2018, doi: 10.1016/j.egypro.2018.09.226.10.1016/j.egypro.2018.09.226
  17. 17. Y. B. A. Farag and A. I. Ölçer, ‘The development of a ship performance model in varying operating conditions based on ANN and regression techniques’, Ocean Engineering, vol. 198, no. July 2019, 2020, doi: 10.1016/j. oceaneng.2020.106972.
  18. 18. L. Bui-Duy and N. Vu-Thi-Minh, ‘Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia’, Asian Journal of Shipping and Logistics, 2020, doi: 10.1016/j.ajsl.2020.04.003.10.1016/j.ajsl.2020.04.003
  19. 19. H. Cui, O. Turan, and P. Sayer, ‘Learning-based ship design optimization approach’, CAD Computer Aided Design, vol. 44, no. 3, pp. 186–195, 2012, doi: 10.1016/j.cad.2011.06.011.10.1016/j.cad.2011.06.011
  20. 20. M. Peker, O. Özkaraca, and B. Kesimal, ‘Modeling heating and cooling loads by regression-based machine learning techniques for energy-efficient building design’, International Journal of Informatics Technologies, pp. 443–449, 2017, doi: 10.17671/gazibtd.310154.10.17671/gazibtd.310154
  21. 21. V. Bertram and H. Schneekluth, Ship Design for Efficiency and Economy. Elsevier, 1998.
DOI: https://doi.org/10.2478/pomr-2021-0008 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
Language: English
Page range: 83 - 96
Published on: Apr 30, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Fatih Okumuş, Araks Ekmekçioğlu, Selin Soner Kara, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution 4.0 License.