Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches

1 Nguyen HP, Nguyen CTU, Tran TM, Dang QH, Pham NDK. Artificial intelligence and machine learning for green shipping: Navigating towards sustainable maritime practices. JOIV Int J Informatics Vis 2024;8:1. https://doi.org/10.62527/joiv.8.1.2581. Search in Google Scholar

2 Duc BV, Nguyen HP. A comprehensive review on big data-based potential applications in marine shipping management. Int J Adv Sci Eng Inf Technol 2021;11:1067–77. Search in Google Scholar

3 UNCTAD. Review of Maritime Report 2021. 2021. Search in Google Scholar

4 Zeńczak W, Gromadzińska AK. Preliminary analysis of the use of solid biofuels in a ship’s power system. Polish Marit Res 2020;27:67–79. https://doi.org/10.2478/pomr-2020-0067. Search in Google Scholar

5 Hoang AT, Foley AM, Nižetić S, Huang Z, Ong HC, Ölçer AI, et al. Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway. J Clean Prod 2022;355:131772. https://doi.org/10.1016/j.jclepro.2022.131772. Search in Google Scholar

6 Vakili S, Ölçer AI, Schönborn A, Ballini F, Hoang AT. Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study. Int J Energy Res 2022;46:20624–49. https://doi.org/10.1002/er.7649. Search in Google Scholar

7 Su W, Zhang Q, Liu Y. Event-triggered adaptive neural network trajectory tracking control for underactuated ships under uncertain disturbance. Polish Marit Res 2023;30:119–31. https://doi.org/doi:10.2478/pomr-2023-0045. Search in Google Scholar

8 Sahnen D, Harmsen J, Nesterova N, Bekdemir C, Van Kranenburg K, Andersson K, et al. Reducing sulphur emissions from ships - The impact of international regulation. Int J Hydrogen Energy 2020;12:32–5. Search in Google Scholar

9 Le TT, Sharma P, Pham NDK, Le DTN, Le VV, Osman SM, et al. Development of comprehensive models for precise prognostics of ship fuel consumption. J Mar Eng Technol 2024:1–15. https://doi.org/10.1080/20464177.2024.2372888. Search in Google Scholar

10 Hoang AT, Tran VD, Dong VH, Le AT. An experimental analysis on physical properties and spray characteristics of an ultrasound-assisted emulsion of ultra-low-sulphur diesel and Jatropha-based biodiesel. J Mar Eng Technol 2022;21:73–81. https://doi.org/10.1080/20464177.2019.1595355. Search in Google Scholar

11 Nguyen VN, Chung N, Balaji GN, Rudzki K, Hoang AT. Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction. Alexandria Eng J 2025;118:664- 80. https://doi.org/10.1016/j.aej.2025.01.067. Search in Google Scholar

12 Nguyen HP, Hoang AT, Nizetic S, Nguyen XP, Le AT, Luong CN, et al. The electric propulsion system as a green solution for management strategy of CO2 emission in ocean shipping: A comprehensive review. Int Trans Electr Energy Syst 2021;31:e12580. https://doi.org/10.1002/2050-7038.12580. Search in Google Scholar

13 Mallouppas G, Yfantis EA. Decarbonization in shipping industry: A review of research, technology development, and innovation proposals. J Mar Sci Eng 2021;9:415. https://doi.org/10.3390/jmse9040415. Search in Google Scholar

14 Herdzik J. Decarbonization of marine fuels—The future of shipping. Energies 2021;14:4311. https://doi.org/10.3390/en14144311. Search in Google Scholar

15 Hoang AT, Pandey A, Martinez De Osés FJ, Chen W-H, Said Z, Ng KH, et al. Technological solutions for boosting hydrogen role in decarbonization strategies and net-zero goals of world shipping: Challenges and perspectives. Renew Sustain Energy Rev 2023;188:113790. https://doi.org/10.1016/j.rser.2023.113790. Search in Google Scholar

16 IMO. Fourth IMO GHG Study 2020 full report. Int Marit Organ 2021;6:524. Search in Google Scholar

17 Chen Y, Sun B, Xie X, Li X, Li Y, Zhao Y. Short-term forecasting for ship fuel consumption based on deep learning. Ocean Eng 2024;301:117398. https://doi.org/10.1016/j.oceaneng.2024.117398. Search in Google Scholar

18 Mantoju CD. Analysis of MARPOL implementation based on port state control statistics. J Int Marit Safety, Environ Aff Shipp 2021;5:132–45. https://doi.org/10.1080/25725084.2021.1965281. Search in Google Scholar

19 Schinas O, Stefanakos CN. Selecting technologies towards compliance with MARPOL Annex VI: The perspective of operators. Transp Res Part D Transp Environ 2014;28:28–40. https://doi.org/10.1016/j.trd.2013.12.006. Search in Google Scholar

20 Hussain N, Khan A, Shumaila, Memon S. Addressing marine pollution: An analysis of MARPOL 73/78 regulations and global implementation efforts. J Soc Sci Rev 2023;3:572–89. https://doi.org/10.54183/jssr.v3i1.193. Search in Google Scholar

21 Čampara L, Hasanspahić N, Vujičić S. Overview of MARPOL ANNEX VI regulations for prevention of air pollution from marine diesel engines. SHS Web Conf 2018;58:01004. https://doi.org/10.1051/shsconf/20185801004. Search in Google Scholar

22 MEPC. MEPC 67 October 2014. Search in Google Scholar

23 Chircop A. The IMO initial strategy for the reduction of GHGs from international shipping: A commentary. Int J Mar Coast Law 2019;34:482–512. https://doi.org/10.1163/15718085-13431093. Search in Google Scholar

24 Joung T-H, Kang S-G, Lee J-K, Ahn J. The IMO initial strategy for reducing greenhouse gas (GHG) emissions, and its follow-up actions towards 2050. J Int Marit Safety, Environ Aff Shipp 2020;4:1–7. https://doi.org/10.1080/25725084.2019.1707938. Search in Google Scholar

25 Nguyen VN, Rudzki K, Dzida M, Pham NDK, Pham MT, Nguyen PQP, et al. Understanding fuel saving and clean fuel strategies towards green maritime. Polish Marit Res 2023;30:146–64. https://doi.org/10.2478/pomr-2023-0030. Search in Google Scholar

26 Ghaemi MH, Zeraatgar H. Impact of propeller emergence on hull, propeller, engine, and fuel consumption performance in regular head waves. Polish Marit Res 2022;29:56–76. https://doi.org/10.2478/pomr-2022-0044. Search in Google Scholar

27 Korczewski Z. Energy and emission quality ranking of newly produced low-sulphur marine fuels. Polish Marit Res 2022;29:77–87. https://doi.org/10.2478/pomr-2022-0045. Search in Google Scholar

28 Zhang M, Tsoulakos N, Kujala P, Hirdaris S. A deep learning method for the prediction of ship fuel consumption in real operational conditions. Eng Appl Artif Intell 2024;130:107425. https://doi.org/10.1016/j.engappai.2023.107425. Search in Google Scholar

29 Agand P, Kennedy A, Harris T, Bae C, Chen M, Park EJ. Fuel consumption prediction for a passenger ferry using machine learning and in-service data: A comparative study. Ocean Eng 2023;284:115271. https://doi.org/10.1016/j.oceaneng.2023.115271. Search in Google Scholar

30 Chen ZS, Lam JSL, Xiao Z. Prediction of harbour vessel fuel consumption based on machine learning approach. Ocean Eng 2023;278:114483. https://doi.org/10.1016/j.oceaneng.2023.114483. Search in Google Scholar

31 Yan R, Wang S, Du Y. Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship. Transp Res Part E Logist Transp Rev 2020;138:101930. https://doi.org/10.1016/j.tre.2020.101930. Search in Google Scholar

32 Rudzki K, Gomulka P, Hoang AT. Optimization model to manage ship fuel consumption and navigation time. Polish Marit Res 2022;29:141–53. https://doi.org/10.2478/pomr-2022-0034. Search in Google Scholar

33 Shanmugasundar G, Vanitha M, Čep R, Kumar V, Kalita K, Ramachandran M. A comparative study of linear, random forest and AdaBoost regressions for modeling non-traditional machining. Processes 2021;9:2015. https://doi.org/10.3390/pr9112015. Search in Google Scholar

34 Quirk TJ. Correlation and simple linear regression. Excel 2016 Appl. Stat. High Sch. Students, Cham: Springer International Publishing; 2018, pp. 107–52. https://doi.org/10.1007/978-3-319-89993-0_6. Search in Google Scholar

35 Breiman L. Random forests. Mach Learn 2001;45:5–32. https://doi.org/10.1023/A:1010933404324. Search in Google Scholar

36 Gnecco N, Terefe EM, Engelke S. Extremal random forests. J Am Stat Assoc 2024:1–24. https://doi.org/10.1080/01621459.2023.2300522. Search in Google Scholar

37 Speiser JL, Miller ME, Tooze J, Ip E. A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl 2019;134:93–101. https://doi.org/10.1016/j.eswa.2019.05.028. Search in Google Scholar

38 Sprenger M, Schemm S, Oechslin R, Jenkner J. Nowcasting Foehn wind events using the AdaBoost machine learning algorithm. Weather Forecast 2017;32:1079–99. https://doi.org/10.1175/WAF-D-16-0208.1. Search in Google Scholar

39 Kyeremeh F, Zhi F, Yi Y, Gyamfi E, Nti IK. Solar PV power forecasting with a hybrid LSTM-AdaBoost ensemble. 2022 IEEE/IET Int. Util. Conf. Expo., IEEE; 2022, pp. 1–7. https://doi.org/10.1109/IUCE55902.2022.10079424. Search in Google Scholar

40 Chen T, Guestrin C. XGBoost. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., New York, NY, USA: ACM; 2016, pp. 785–94. https://doi.org/10.1145/2939672.2939785. Search in Google Scholar

41 Darmawan H, Yuliana M, Hadi MZS. GRU and XGBoost performance with hyperparameter tuning using GridSearchCV and Bayesian optimization on an IoT-based weather prediction system. Int J Adv Sci Eng Inf Technol 2023;13:848–59. https://doi.org/10.18517/ijaseit.13.3.18377. Search in Google Scholar

42 Abdi J, Hadavimoghaddam F, Hadipoor M, Hemmati-Sarapardeh A. Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models. Sci Rep 2021;11:24468. https://doi.org/10.1038/s41598-021-04168-w. Search in Google Scholar

43 Sagi O, Rokach L. Approximating XGBoost with an interpretable decision tree. Inf Sci (Ny) 2021;572:522–42. https://doi.org/10.1016/j.ins.2021.05.055. Search in Google Scholar

44 Jumin E, Basaruddin FB, Yusoff YBM, Latif SD, Ahmed AN. Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia. Environ Sci Pollut Res 2021;28:26571–83. https://doi.org/10.1007/s11356-021-12435-6. Search in Google Scholar

45 Sharma KV, Talpa Sai PHVS, Sharma P, Kanti PK, Bhramara P, Akilu S. Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids’ thermophysical profile using an explainable artificial intelligence (XAI) approach. Eng Appl Artif Intell 2023;126:106967. https://doi.org/10.1016/j.engappai.2023.106967. Search in Google Scholar

46 Alruqi M, Sharma P, Algburi S, Khan MA, Alsubih M, Islam S. Biomass energy transformation: Harnessing the power of explainable AI to unlock the potential of ultimate analysis data. Environ Technol Innov 2024;35:103652. https://doi.org/10.1016/j.eti.2024.103652. Search in Google Scholar

47 de Myttenaere A, Golden B, Le Grand B, Rossi F. Mean absolute percentage error for regression models. Neurocomputing 2016;192:38–48. https://doi.org/10.1016/j.neucom.2015.12.114. Search in Google Scholar

48 Elvidge S, Angling MJ, Nava B. On the use of modified Taylor diagrams to compare ionospheric assimilation models. Radio Sci 2014;49:737–45. https://doi.org/10.1002/2014RS005435. Search in Google Scholar

49 Simão ML, Videiro PM, Silva PBA, de Freitas Assad LP, Sagrilo LVS. Application of Taylor diagram in the evaluation of joint environmental distributions’ performances. Mar Syst Ocean Technol 2020;15:151–9. https://doi.org/10.1007/s40868-020-00081-5. Search in Google Scholar

50 Aberasturi DT. Violin plot. Wiley StatsRef Stat. Ref. Online, Wiley; 2023, pp. 1–7. https://doi.org/10.1002/9781118445112.stat08426. Search in Google Scholar

51 Molina E, Viale L, Vazquez P. How should we design violin plots? 2022 IEEE 4th Work. Vis. Guidel. Res. Des. Educ., IEEE; 2022, pp. 1–7. https://doi.org/10.1109/VisGuides57787.2022.00006. Search in Google Scholar

52 Hintze JL, Nelson RD. Violin plots: A box plot-density trace synergism. Am Stat 1998;52:181. https://doi.org/10.2307/2685478. Search in Google Scholar

53 Tanious R, Manolov R. Violin plots as visual tools in the meta-analysis of single-case experimental designs. Methodology 2022;18:221–38. https://doi.org/10.5964/meth.9209. Search in Google Scholar

54 Vergura S. Supervision of the energy performance of multi-arrays PV plants by means of bi-monthly violin plots. Renew Energy Power Qual J 2022;20:625–8. https://doi.org/10.24084/repqj20.384. Search in Google Scholar

55 Shin J, Yim I, Kwon S-B, Park S, Kim M, Cha Y. Evaluation of temperature effects on brake wear particles using clustered heatmaps. Environ Eng Res 2019;24:680–9. https://doi.org/10.4491/eer.2018.385. Search in Google Scholar