References
- Inal OB. Decarbonization of shipping: Hydrogen and fuel cells legislation in the maritime industry. Brodogradnja 2024;75:1–13. https://doi.org/10.21278/brod75205.
- 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.
- 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.
- Eski Ö, Tavacioglu L. A combined method for the evaluation of contributing factors to maritime dangerous goods transport accidents. Brodogradnja 2024;75:1–20. https://doi.org/10.21278/brod75408.
- Gao J, Zhang Y. Ship collision avoidance decision-making research in coastal waters considering uncertainty of target ships. Brodogradnja 2024;75:1–16. https://doi.org/10.21278/brod75203.
- Alamoush AS, Ölçer AI. Harnessing cutting-edge technologies for sustainable future shipping: An overview of innovations, drivers, barriers, and opportunities. Marit Technol Res 2025;7:277313. https://doi.org/10.33175/mtr.2025.277313.
- Hussein K, Song D-W. Maritime Logistics for the Next Decade: Challenges, Opportunities and Required Skills. Glob. Logist. Supply Chain Strateg. 2020s, Cham: Springer International Publishing; 2023, p. 151–74. https://doi.org/10.1007/978-3-030-95764-3_9.
- Le TT, Nguyen HP, Rudzki K, Rowiński L, Bui VD, Truong TH, et al. Management Strategy for Seaports Aspiring to Green Logistical Goals of IMO: Technology and Policy Solutions. Polish Marit Res 2023;30:165–87. https://doi.org/10.2478/pomr-2023-0031.
- Nguyen MD, Yeon KT, Rudzki K, Nguyen HP, Pham NDK. Strategies for developing logistics centers: Technological trends and policy implications. Polish Marit Res 2023;30:129–47. https://doi.org/10.2478/pomr-2023-0066.
- Karczewski A, Kozak J. A Generative Approach to Hull Design for a Small Watercraft. Polish Marit Res 2023;30:4–12. https://doi.org/doi:10.2478/pomr-2023-0001.
- Huynh VC, Tran GT. Improving the accuracy of ship resistance prediction using computational fluid dynamics tool. Int J Adv Sci Eng Inf Technol 2020;10:171–7. https://doi.org/10.18517/ijaseit.10.1.10588.
- Hoang AT, Bui TAE, Nguyen XP, Bui VH, Nguyen QC, Truong TH, et al. Explainable machine learning-based prediction of fuel consumption in ship main engines using operational data. Brodogradnja 2025;76:1–24. https://doi.org/10.21278/brod76405.
- 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.
- Nguyen HP, Pham NDK, Bui VD. Technical-Environmental Assessment of Energy Management Systems in Smart Ports. Int J Renew Energy Dev 2022;11:889–901. https://doi.org/10.14710/ijred.2022.46300.
- 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.
- Suárez de la Fuente S, Cao T, Pujol AG, Romagnoli A. Waste heat recovery on ships. Sustain. Energy Syst. Ships, Elsevier; 2022, p. 123–95. https://doi.org/10.1016/B978-0-12-824471-5.00011-6.
- Hoang AT. Waste heat recovery from diesel engines based on Organic Rankine Cycle. Appl Energy 2018;231:138–66.
- Kowalski J, Tarelko W. NOx emission from a two-stroke ship engine: Part 2 - Laboratory test. Appl Therm Eng 2009. https://doi.org/10.1016/j.applthermaleng.2008.06.031.
- Nguyen VN, Nguyen AX, Nguyen DT, Le HC, Nguyen VP. A Comprehensive Understanding of Bainite Phase Transformation Mechanism in TRIP Bainitic-supported Ferrite Steel. Int J Adv Sci Eng Inf Technol 2024;14:309–25. https://doi.org/10.18517/ijaseit.14.1.19706.
- Gavalas D. Green finance frameworks for sustainable shipping industry and blue economy: A review. Marit Technol Res 2025;7:Manuscript. https://doi.org/10.33175/mtr.2025.277132.
- Priya JC, Rudzki K, Nguyen XH, Nguyen HP, Chotechuang N, Pham NDK. Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics. Polish Marit Res 2024;31:135–45. https://doi.org/10.2478/pomr-2024-0014.
- Genç Y, Kafali M, Çelebi UB. Approximate Estimation of Man-Day in Ship Block Production: A Two-Stage Stochastic Program. Polish Marit Res 2024;31:146–58. https://doi.org/doi:10.2478/pomr-2024-0015.
- Liang L, Baoji Z, Hao Z, Jiaye G, Zheng T, Shuhui G, et al. Study on numerical simulation and mitigation of parametric rolling in a container ship under head waves. Brodogradnja 2024;75:1–19. https://doi.org/10.21278/brod75305.
- Wu B, Zhang J, Yip TL, Guedes Soares C. A quantitative decision-making model for emergency response to oil spill from ships. Marit Policy Manag 2021;48:299–315. https://doi.org/10.1080/03088839.2020.1791994.
- Zhang W, Li C, Chen J, Wan Z, Shu Y, Song L, et al. Governance of global vessel-source marine oil spills: Characteristics and refreshed strategies. Ocean Coast Manag 2021;213:105874. https://doi.org/10.1016/j.ocecoaman.2021.105874.
- Chen J, Zhang W, Wan Z, Li S, Huang T, Fei Y. Oil spills from global tankers: Status review and future governance. J Clean Prod 2019;227:20–32. https://doi.org/10.1016/j.jclepro.2019.04.020.
- Le QD, Pham D, Bui TAE, Nguyen LH, Nguyen PQP, Dang TN. Ensemble Machine Learning Model Prediction and Metaheuristic Optimisation of Oil Spills Using Organic Absorbents: Supporting Sustainable Maritime. Polish Marit Res 2025;32:141–55. https://doi.org/10.2478/pomr-2025-0029.
- Onyena AP, Nwaogbe OR. Assessment of water quality and heavy metal contamination in ballast water: Implications for marine ecosystems and human health. Marit Technol Res 2024;6:270227. https://doi.org/10.33175/mtr.2024.270227.
- Soria‐Rodríguez C. The international regulation for the protection of the environment in the development of marine renewable energy in the EU. Rev Eur Comp Int Environ Law 2021;30:46–60. https://doi.org/10.1111/reel.12337.
- Podsiadlo A, Tarelko W. Modelling and developing a decision-making process of hazard zone identification in ship power plants. Int J Press Vessel Pip 2006. https://doi.org/10.1016/j.ijpvp.2006.02.017.
- Negreiros ACSV de, Lins ID, Maior CBS, Moura MJ das C. Oil spills characteristics, detection, and recovery methods: A systematic risk-based view. J Loss Prev Process Ind 2022;80:104912. https://doi.org/10.1016/j.jlp.2022.104912.
- Anju M, Renuka NK. Magnetically actuated graphene coated polyurethane foam as potential sorbent for oils and organics. Arab J Chem 2020. https://doi.org/10.1016/j.arabjc.2018.01.012.
- Chau MQ, Truong TT, Hoang AT, Le TH. Oil spill cleanup by raw cellulose-based absorbents: a green and sustainable approach. Energy Sources, Part A Recover Util Environ Eff 2025;47:8269–82. https://doi.org/10.1080/15567036.2021.1928798.
- Zhao S, Yin L, Zhou Q, Liu C, Zhou K. In situ self-assembly of zeolitic imidazolate frameworks on the surface of flexible polyurethane foam: Towards for highly efficient oil spill cleanup and fire safety. Appl Surf Sci 2020;506:144700.
- Hoang AT, Pham XD. An investigation of remediation and recovery of oil spill and toxic heavy metal from maritime pollution by a new absorbent material. J Mar Eng Technol 2021;20:159–69. https://doi.org/10.1080/20464177.2018.1544401.
- Jayarathna MD, Rajapaksha AU, Samarasekara S, Vithanage M. Oil Spill Response: Existing Technologies, Prospects and Perspectives. CleanMat 2024;1:78–96. https://doi.org/10.1002/clem.17.
- Vasconcelos RN, Lima ATC, Lentini CAD, Miranda JG V., de Mendonça LFF, Lopes JM, et al. Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges. J Mar Sci Eng 2023;11:1406. https://doi.org/10.3390/jmse11071406.
- Liu P, Zhao Y, Liu B, Li Y, Chen P. Oil spill extraction from X-band marine radar images by power fitting of radar echoes. Remote Sens Lett 2021;12:345–52. https://doi.org/10.1080/2150704X.2021.1892852.
- Ma X, Xu J, Pan J, Yang J, Wu P, Meng X. Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment. J Environ Manage 2023;325:116637. https://doi.org/10.1016/j.jenvman.2022.116637.
- Al-Ruzouq R, Gibril MBA, Shanableh A, Kais A, Hamed O, Al-Mansoori S, et al. Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review. Remote Sens 2020;12:3338. https://doi.org/10.3390/rs12203338.
- Dong X, Li J, Li B, Jin Y, Miao S. Marine Oil Spill Detection from Low-Quality SAR Remote Sensing Images. J Mar Sci Eng 2023;11:1552. https://doi.org/10.3390/jmse11081552.
- Wang B, Shao Q, Song D, Li Z, Tang Y, Yang C, et al. A Spectral-Spatial Features Integrated Network for Hyperspectral Detection of Marine Oil Spill. Remote Sens 2021;13:1568. https://doi.org/10.3390/rs13081568.
- Trujillo-Acatitla R, Tuxpan-Vargas J, Ovando-Vázquez C. Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach. Mar Pollut Bull 2022;184:114132. https://doi.org/10.1016/j.marpolbul.2022.114132.
- Shah P, Zaveri T, Kumar R, Sharma S, Patel D. Research oriented foss solution for automatic oil spill detection using risat-1 sar data. 2017 IEEE Int. Geosci. Remote Sens. Symp., IEEE; 2017, p. 3121–4. https://doi.org/10.1109/IGARSS.2017.8127659.
- Kang S-G, Joung TH, Lee S, Lee J-Y, You Y. The level of oil spill risk in Northwest Pacific and regional cooperation activities of NOWPAP MERRAC on marine pollution preparedness and response. Int Oil Spill Conf Proc 2021;2021. https://doi.org/10.7901/2169-3358-2021.1.1141537.
- Abhijith A, Rameshbabu H. Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application. Int J Adv Comput Sci Appl 2021;12. https://doi.org/10.14569/IJACSA.2021.0120523.
- Satyanarayana AR, Dhali MA. Oil Spill Segmentation using Deep Encoder-Decoder models. Cornell Univiersity 2023.
- Liu X, Lu D, Zhang A, Liu Q, Jiang G. Data-Driven Machine Learning in Environmental Pollution: Gains and Problems. Environ Sci Technol 2022;56:2124–33. https://doi.org/10.1021/acs.est.1c06157.
- seyyedi S reza, Kowsari E, Ramakrishna S, Gheibi M, Chinnappan A. Marine plastics, circular economy, and artificial intelligence: A comprehensive review of challenges, solutions, and policies. J Environ Manage 2023;345:118591. https://doi.org/10.1016/j.jenvman.2023.118591.
- Li K, Yu H, Xu Y, Luo X. Detection of Marine Oil Spills Based on HOG Feature and SVM Classifier. J Sensors 2022;2022:1–11. https://doi.org/10.1155/2022/3296495.
- Temitope Yekeen S, Balogun A, Wan Yusof KB. A novel deep learning instance segmentation model for automated marine oil spill detection. ISPRS J Photogramm Remote Sens 2020;167:190–200. https://doi.org/10.1016/j.isprsjprs.2020.07.011.
- Zhu Q, Zhang Y, Li Z, Yan X, Guan Q, Zhong Y, et al. Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images. IEEE Trans Geosci Remote Sens 2022;60:1–10. https://doi.org/10.1109/TGRS.2021.3115492.
- Duan P, Kang X, Ghamisi P, Li S. Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection With an Isolation Forest-Guided Unsupervised Detector. IEEE Trans Geosci Remote Sens 2023;61:1–11. https://doi.org/10.1109/TGRS.2023.3268944.
- Raharjo D, Solechudin M. Detect oil spill in offshore facility using convolutional neural network and transfer learning. Proceedings, Indones Pet Assoc Forty-Fifth Annu Conv Exhib 2021.
- Trongtirakul T, Agaian S, Oulefki A, Panetta K. Method for Remote Sensing Oil Spill Applications Over Thermal and Polarimetric Imagery. IEEE J Ocean Eng 2023;48:973–87. https://doi.org/10.1109/JOE.2023.3245759.
- Wang D, Liu S, Zhang C, Xu M, Yang J, Yasir M, et al. An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image. Mar Pollut Bull 2023;192:114981. https://doi.org/10.1016/j.marpolbul.2023.114981.
- Ghonaime TW, Farouk Al-Sadek A. OilSpillNet: Deep Learning Model for Synthetic Aperture Radar Imagery Oil Spill Detection. 2023 Intell. Methods, Syst. Appl., IEEE; 2023, p. 412–7. https://doi.org/10.1109/IMSA58542.2023.10217532.
- J SM, V P. An automatic approach for the detection of oil spill using optimized deep learning in synthetic aperture radar images. Energy Sources, Part A Recover Util Environ Eff 2023;45:6772–87. https://doi.org/10.1080/15567036.2023.2216153.
- Ozigis MSJ. Detection and Mapping of Terrestrial Oil Spill Impact Using Remote Sensing Data in Combination with Machine Learning Methods. A Case Site within the Niger Delta Region of Nigeria. University of Leicester, 2019.
- Li C, Wang M, Yang X, Chu D. DS-UNet: Dual-Stream U-Net for Oil Spill Detection of SAR Image. IEEE Geosci Remote Sens Lett 2023;20:1–5. https://doi.org/10.1109/LGRS.2023.3330957.
- Yang Y-J, Singha S, Mayerle R. A deep learning based oil spill detector using Sentinel-1 SAR imagery. Int J Remote Sens 2022;43:4287–314. https://doi.org/10.1080/01431161.2022.2109445.
- Zhang Y, Zhu Q, Guan Q. Oil Spill Detection Based on CBD-Net Using Marine SAR Image. 2021 IEEE Int. Geosci. Remote Sens. Symp. IGARSS, IEEE; 2021, p. 3495–8. https://doi.org/10.1109/IGARSS47720.2021.9553884.
- Ahmed S, ElGharbawi T, Salah M, El-Mewafi M. Deep neural network for oil spill detection using Sentinel-1 data: application to Egyptian coastal regions. Geomatics, Nat Hazards Risk 2023;14:76–94. https://doi.org/10.1080/19475705.2022.2155998.
- Ghorbani Z, Behzadan AH. Monitoring offshore oil pollution using multi-class convolutional neural networks. Environ Pollut 2021;289:117884. https://doi.org/10.1016/j.envpol.2021.117884.
- Yang Y-J, Singha S, Mayerle R. Fully Automated Sar Based Oil Spill Detection Using Yolov4. 2021 IEEE Int. Geosci. Remote Sens. Symp. IGARSS, IEEE; 2021, p. 5303–6. https://doi.org/10.1109/IGARSS47720.2021.9553030.
- Shaban M, Salim R, Abu Khalifeh H, Khelifi A, Shalaby A, El-Mashad S, et al. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors 2021;21:2351. https://doi.org/10.3390/s21072351.
- Bianchi FM, Espeseth MM, Borch N. Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning. Remote Sens 2020;12:2260. https://doi.org/10.3390/rs12142260.
- Ma X, Xu J, Wu P, Kong P. Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information From Sentinel-1 SAR Images. IEEE Trans Geosci Remote Sens 2022;60:1–13. https://doi.org/10.1109/TGRS.2021.3126175.
- Zhang J, Feng H, Luo Q, Li Y, Zhang Y, Li J, et al. Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using Superpixel-Level Image Stretching and Deep Convolutional Neural Network. Remote Sens 2022;14:3900. https://doi.org/10.3390/rs14163900.
- Dai Z, Liang H, Duan T. Small-Sample Sonar Image Classification Based on Deep Learning. J Mar Sci Eng 2022;10:1820. https://doi.org/10.3390/jmse10121820.
- Barzegar F, Seydi ST, Farzaneh S, Sharifi MA. Oil spill detection in the Caspian sea with a SAR image using a densenet model. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2023;X-4/W1-202:95–100. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-95-2023.
- UNCTAD. Review of Maritime Transport 2024: Navigating maritime chokepoints 2024.
- Alghamdi R, Saeed N, Dahrouj H, Al-Naffouri TY, Alouini M-S. On Distributed Routing in Underwater Optical Wireless Sensor Networks. Cornell Univiersity 2018.
- Zhang Y, Sam EK, Liu J, Lv X. Biomass-Based/Derived Value-Added Porous Absorbents for Oil/Water Separation. Waste and Biomass Valorization 2023;14:3147–68. https://doi.org/10.1007/s12649-023-02112-9.
- Hoang AT, Nižetić S, Duong XQ, Rowinski L, Nguyen XP. Advanced super-hydrophobic polymer-based porous absorbents for the treatment of oil-polluted water. Chemosphere 2021;277. https://doi.org/10.1016/j.chemosphere.2021.130274.
- Hoang AT, Nguyen XP, Duong XQ, Huynh TT. Sorbent-based devices for the removal of spilled oil from water: a review. Environ Sci Pollut Res 2021;28:28876–910. https://doi.org/10.1007/s11356-021-13775-z.
- Saildrone. Saildrone: Defense, Commercial & Science Data Solutions. Saildrone 2024.
- Cardoso CKM, Moreira ÍTA, de Souza Queiroz AF, de Oliveira OMC, de Carvalho Lima Lobato AK. Bio-Based Sorbents for Marine Oil Spill Response: Advances in Modification, Circularity, and Waste Valorization. Resources 2025;14:140. https://doi.org/10.3390/resources14090140.
- Haridharan N, Sundar D, Kurrupasamy L, Anandan S, Liu C, Wu JJ. Oil spills adsorption and cleanup by polymeric materials: A review. Polym Adv Technol 2022;33:1353–84. https://doi.org/10.1002/pat.5636.
- Visco A, Quattrocchi A, Nocita D, Montanini R, Pistone A. Polyurethane Foams Loaded with Carbon Nanofibers for Oil Spill Recovery: Mechanical Properties under Fatigue Conditions and Selective Absorption in Oil/Water Mixtures. Nanomaterials 2021;11:735.
- Hoang AT, Le VV, Al-Tawaha ARMS, Nguyen DN, Al-Tawaha ARMS, Noor MM, et al. An absorption capacity investigation of new absorbent based on polyurethane foams and rice straw for oil spill cleanup. Pet Sci Technol 2018;36:361–70. https://doi.org/10.1080/10916466.2018.1425722.
- Li H, Lin S, Feng X, Pan Q. Preparation of superhydrophobic and superoleophilic polyurethane foam for oil spill cleanup. J Macromol Sci Part A 2021;58:758–68. https://doi.org/10.1080/10601325.2021.1934013.