Digital Twin and AI in Marine Systems: A Bibliometric Analysis from an Information Systems Perspective

Authors

  • Aditya Lapu Kalua Diponegoro University; Sam Ratulangi University, Indonesia
  • Mochamad Agung Wibowo Diponegoro University, Indonesia
  • Luther Alexander Latumakulita Sam Ratulangi University, Indonesia
Pages Icon

DOI:

https://doi.org/10.63158/journalisi.v8i4.1717

Keywords:

Digital Twin, Artificial Intelligence, Marine Systems, Bibliometric Analysis, Scientometric Mapping, Information Systems, Environmental Monitoring

Abstract

Marine Digital Twin (DT) and Artificial Intelligence (AI) research spans maritime engineering, environmental monitoring, and governance contexts. This study provides a Scopus-based bibliometric and scientometric mapping of 289 English-language documents published from 2020 through April 10, 2026. VOSviewer and the Bibliometrix R-package were used for keyword co-occurrence mapping and descriptive analysis. Annual output was 4, 9, 24, 37, 50, 108, and 57 documents for 2020-2026, respectively; 2026 is a partial-year observation. China (n = 78), the United Kingdom (n = 31), and the United States (n = 27) were the leading countries. University College London (n = 10), Ningbo University (n = 8), and Dr. D. Y. Patil Institute of Technology (n = 7) had the highest affiliation counts. Three clusters were identified consistently: Industrial Maritime Applications, AI and Algorithmic Methods, and Environmental Monitoring and Earth Systems. The corpus remains dominated by engineering and offshore infrastructure, while the smaller environmental cluster does not by itself demonstrate a transition toward conservation. The Information Systems contribution is framed through data governance, semantic interoperability, platform ecosystems, and decision-support systems.

Downloads

Download data is not yet available.

References

[1] N. S. Madusanka, Y. Fan, S. Yang, and X. Xiang, “Digital twin in the maritime domain: A review and emerging trends,” J. Mar. Sci. Eng., vol. 11, no. 6, Art. no. 1210, 2023, doi: 10.3390/jmse11061210.

[2] Y. Xie, H. J. Kim, Y. Yin, K. Liu, T. Smith, and J. K. Paik, “Enhancing the safety and sustainability of aging jacket-type offshore wind turbines in extreme weather conditions through digital healthcare engineering: A literature review,” Ships Offshore Struct., 2025.

[3] Y. Ye, A. González-Vidal, A. Cisterna-García, A. Pérez-Ruzafa, M. A. Zamora Izquierdo, and A. F. Skarmeta, “Advancing towards a marine digital twin platform: Modeling the Mar Menor coastal lagoon ecosystem in the southwestern Mediterranean,” Future Gener. Comput. Syst., vol. 178, pp. 1–15, 2026.

[4] S. Wang, S. Qiu, Z. Sun, T.-H. Hsieh, F. Qian, and Y. Xiao, “Single-view 3D object perception based on vessel generative adversarial network for autonomous ships,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 8, pp. 9238–9252, Aug. 2024, doi: 10.1109/TITS.2024.336xxxx.

[5] J. Muñoz and R. P. Fernández, “Practical guide for adopting Industry 4.0 in CAD systems,” Proc. RINA Conf., pp. 1–7, 2019.

[6] J. P. Roselyn, N. Vedachalam, B. N. Jyothi, U. Sowmmiya, D. Gobichandhru, and S. Ramesh, “Cognitive digital twin for emergency survival decision-support in deep-ocean submersible Matsya6000,” J. Mar. Sci. Appl., 2026.

[7] F. Zhou, K. Yu, W. Xie, J. Lyu, Z. Zheng, and S. Zhou, “Digital twin-enabled smart maritime logistics management in the context of Industry 5.0,” IEEE Access, vol. 12, pp. 1–15, 2024.

[8] H. Taghavifar, “Creating a digital twin platform for maritime decarbonization by AI-assisted CII measure prediction: A case of chemical tanker,” Marit. Transp. Res., vol. 9, 2025.

[9] M. B. Purss, “Earth observation data processing revolution: TerraNexus DGGS platform for digital twins and federated systems,” in Proc. Int. Astronaut. Congr. (IAC), 2025, pp. 740–750.

[10] B. Xiao, W. Chen, and H. Chen, “Design and implementation of a virtual maintenance training system for a certain equipment based on digital twin technology,” in Proc. ACM Int. Conf., 2025, pp. 77–85.

[11] Y. Liu, T. Cheng, H. Zheng, C. Wang, Y. Dai, S. Zhang, W. Fan, Z. Wu, and H. Fang, “Preliminary exploration of an informatized management model for deep-sea aquaculture: From land-based farming to offshore marine ranches,” Fishes, vol. 11, no. 3, 2026.

[12] W. Xie, J. Li, J. Shi, X. Zhang, A. S. Usmani, and G. Chen, “Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach,” Mar. Pollut. Bull., vol. 192, Art. no. 115091, 2023.

[13] G. Delnevo, G. Tumedei, V. Ghini, and C. Prandi, “Evaluating the use of machine learning algorithms in environmental sensing for energy saving,” in Proc. ACM Conf., 2023, pp. 201–206.

[14] J. W. Ringsberg and J.-K. Paik, “International collaboration and transformative technologies: Reflections on two decades of ships and offshore structures and a decade of ICSOS,” Ships Offshore Struct., vol. 21, no. 1, pp. 4–7, 2026.

[15] Y. Wei, X. Xiao, H. Ye, C. Lin, Z. Chen, and C. Gong, “Application of artificial intelligence technology in fatigue detection and life prediction of offshore wind turbine blades,” in Proc. ACM Int. Conf., 2025, pp. 1455–1460.

[16] A. G. Bruzzone, M. Gotelli, A. Giovannetti, A. De Paoli, R. Ferrari, B. Gadupuri, J. Karim, E. Cepolina, M. Pedemonte, A. Sciomachen, G. Fancello, A. Taib, and X. Sina, “Safety and security in the marine and harbor environment,” in Proc. I3M Conf., vol. 2023-Sep., 2023.

[17] L. Patanè, C. Iuppa, C. Faraci, and M. G. Xibilia, “A deep hybrid network for significant wave height estimation,” Ocean Model., vol. 189, Art. no. 102394, 2024.

[18] D.-S. Kwon, C. Jin, and M. Kim, “Transformer-based ensemble method for parametric estimation of directional ocean wave spectra from FPSO vessel motions,” in Proc. IEEE Conf., 2025.

[19] E. W. Bergsma, T. Allen, V. Lonjou, S. Baillarin, J. Le Moigne, L. Rogers, B. Smith, J. Walter, P.-M. Brunet, C. Hummer, D. Lallement, and T. Xavier, “SCoast-DT: Creation and deployment of space-based digital twins of coastal zones,” in Proc. IEEE Conf., 2025.

[20] A. Cerrone, L. Westerink, G. Ling, C. Blakely, D. Wirasaet, C. Dawson, and J. Westerink, “Correcting physics-based global tide and storm water level forecasts with the temporal fusion transformer,” Ocean Model., vol. 195, Art. no. 102553, 2025.

[21] J. Kim, H. Hwang, Y. Ko, C.-H. Lee, B. G. Lee, and J. Bae, “Digital twin-based smart aquafarm monitoring system with multi-instance learning based on autoencoder model,” Aquacult. Eng., vol. 112, Art. no. 102620, 2026.

[22] M. Rajić, M. Mančić, A. Glumac, M. Rossi, and C. Rebelo, “Digital twins and AI integration in offshore renewable energy: A review,” J. Phys. Conf. Ser., vol. 1552, 2025.

[23] Y. Liu, X. Liu, J. Guo, R. Lou, and Z. Lv, “Digital twins of wave energy generation based on artificial intelligence,” in Proc. IEEE Conf., pp. 718–719, 2022.

[24] M. Shahin, A. N. Nasr, A. Malhi, S. Bauk, O. V. Banda, P. Kujala, R.-M. Soe, and S. Wang, “Hybrid physics–AI digital twin framework for shared mooring systems in deep-water floating offshore wind farms,” J. Mar. Sci. Appl., 2026.

[25] A. Calonge, R. Develter, C. Muñiz, C. Parcerisas, J. Reubens, W. Boone, K. Deneudt, and E. Debusschere, “Scalable low-cost seabed landers: The missing link for sustained, integrated, long-term observations in dynamic shallow seas,” Remote Sens. Ecol. Conserv., 2026.

[26] X. Feng, J. Zheng, Y. Liu, and Y. Bao, “Scour assessment for offshore wind turbines: A state-of-the-art review,” J. Civ. Struct. Health Monit., vol. 15, no. 7, pp. 2395–2417, 2025.

[27] R. Sudarshan, B. Chinnappan, P. U. Chaithanya, K. Gayathri, and S. Thirumal-Valavan, Nanotechnology-Enhanced Coatings for Corrosion Resistance in Marine, Industrial, and Biomedical Engineering Leveraging AI for Predictive Maintenance. Boca Raton, FL, USA: CRC Press, 2025.

[28] F. Zocco, H.-C. Wang, and M. Van, “Digital twins for marine operations: A brief review on their implementation,” arXiv preprint arXiv:2301.09574, 2023..

[29] Y. Li, D. Yu, L. Yang, L. Zhang, P. Chen, B.-Y. Tao, W. Wang, Q. Kong, R. Dai, B. Miao, S. Chen, S. Qi, J. Wang, and D. Pan, “A review of the applications of digital twin technology in marine research,” China Ocean Eng., 2025, doi: 10.1007/s13344-025-0030-9.

[30] C. Prandi, J. M. Cecilia, P. Manzoni, S. Peña-Haro, D. Pierson, W. Colom, P. Blanco, C. A. García, I. J. Navarro, and J. Senent, “On integrating intelligent infrastructure and participatory monitoring for environmental modelling: The SmartLagoon approach,” in Proc. ACM Int. Conf., pp. 236–243, 2022.

[31] V. Metheniti, M. E. Kallos, and G. Z. Kyriakopoulos, “Recent advancements and challenges in artificial intelligence-enabled digital twins of the ocean,” Climate, vol. 14, no. 1, Art. no. 3, 2025..

[32] S. I. Karnavas, I. Peteinatos, A. Kyriazis, and S. G. Barbounaki, “Using fuzzy multi-criteria decision-making as a human-centered AI approach to adopting new technologies in maritime education in Greece,” Information, vol. 16, no. 4, 2025.

[33] J. Shi, L. Dourthe, D. Li, L. Deng, L. Louback, F. Song, N. Abolins, F. Verano, P. Zhang, J. Groover, D. G. Falla, and K. Li, “Real-time underreamer vibration predicting, monitoring, and decision-making using hybrid modeling and a process digital twin,” SPE Drill. Complet., vol. 38, no. 2, pp. 201–219, 2023.

[34] J. Aguzzi, E. Chatzidouros, D. Chatzievangelou, M. Clavel-Henry, S. Flögel, N. Bahamon, et al., “A digital-twin strategy using robots for marine ecosystem monitoring,” Ecol. Inform., vol. 91, Art. no. 102943, 2025.

[35] A. Boretti, “Recycled carbon composites for high-speed, carbon-negative marine vessels,” Next Sustain., vol. 7, 2026.

[36] J. Wen, J. Yang, Y. Li, J. He, Z. Li, and H. Song, “Behavior-based formation control digital twin for multi-agent UAV in edge computing,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 5, pp. 2791–2801, 2023.

[37] J. Parunov, C. Guedes Soares, S. Hirdaris, and X. Wang, “Uncertainties in modelling the low-frequency wave-induced global loads in ships,” Mar. Struct., vol. 86, Art. no. 103305, 2022.

[38] Y. R. Alkarem, K. Huguenard, R. W. Kimball, and S. T. Grilli, “Uncertainty-guided prediction horizon of phase-resolved ocean wave forecasting under data sparsity: Experimental and numerical evaluation,” J. Mar. Sci. Eng., vol. 13, no. 7, 2025.

[39] Z. Nie, H. Tian, Y. Yin, Y. Zhou, W. Li, Y. Xiong, Y. Wang, Z. Zhang, Y. Yang, D. Xie, M. Wang, and S. Huang, “System-level optimization of AUV swarm control and perception: An energy-aware federated meta-transfer learning framework with digital twin validation,” J. Mar. Sci. Eng., vol. 14, no. 4, 2026.

[40] H. J. Kim, Y. Xie, and J. K. Paik, “Predictive health analysis for future maintenance planning in aging containership hull structures within digital healthcare engineering systems,” in Proc. ASME Conf., vol. 1, 2025.

[41] W. Wang, C. Hou, Z. Wu, L. Wang, J. Gu, and L. He, “Frame and visualization for digital twin of marine riserless well intervention,” Comput. Integr. Manuf. Syst., vol. 27, no. 2, pp. 423–431, 2021.

[42] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, Art. no. n71, 2021.

[43] T. Buckley, J. Yao, S. Peters, G. Kazanjian, R. Blankert, and C. Wever, “Saving lives by integrating space-based solutions,” in Proc. Int. Astronaut. Congr. (IAC), vol. 2023-Oct., 2023.

[44] J. Ranson, V. Lonjou, S. Helfrich, and L. Rogers, “Satellite data for a coastal zone digital twin use case,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pp. 5927–5930, 2024.

[45] M. Chabot and J.-L. Bertrand, “Adaptive flood risk management: A decision support system integrating deep learning, digital twins, and economic risk assessment,” Glob. Environ. Change, vol. 95, Art. no. 102514, 2025.

Downloads

Published

2026-08-29

Issue

Vol. 8 No. 4 (2026): August

Section

Articles

License

Copyright (c) 2026 Journal of Information Systems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

Most read articles by the same author(s)