A Multi-Factor Coupled Framework for Maritime Survival Assessment and Drift Prediction

Ruiying Wang

doi:10.54691/4jd59669

Authors

Ruiying Wang

DOI:

https://doi.org/10.54691/4jd59669

Keywords:

Maritime Emergency Rescue; Survival State Assessment; Trajectory Prediction; Multi-Factor Fusion; Simulation System.

Abstract

Maritime emergency rescue faces significant challenges due to high uncertainty in target localization and the difficulty of quantitatively evaluating survival states. To address these issues, a coupled multi-factor framework is proposed for maritime survival assessment and drift prediction. It integrates environmental dynamics with individual physiological and psychological characteristics through a time-dependent coupling mechanism. A current–wind coupled model is employed for trajectory prediction. A trajectory–survival co-evolution framework is introduced to capture the dynamic interaction between spatial drift and survival degradation. The proposed framework is further implemented within a simulation platform to support dynamic anal-ysis and visualization. Simulation results indicate that the proposed framework provides more consistent and robust trajectory prediction under the constructed maritime conditions. Under ±5% environmental perturbations, trajectory deviation remains within 8% and survival variation is below 6%, with relative per-formance improvements of approximately 10%–15% compared with baseline simulation models under the constructed experimental settings.

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References

[1] Breivik, Ø., Allen, A.A., Maisondieu, C., Roth, J.C.: Wind-induced drift of objects at sea: The leeway field method. Applied Ocean Research 33(2), 100–109 (2011)

[2] Allen, A.A., Plourde, J.V.: Review of leeway: Field experiments and implementation. U.S. Coast Guard Report CG-D-08-99. NTIS, Springfield (1999)

[3] Xu, J., Liu, Y., Zhang, Z.: Numerical simulation of drifting trajectories for search and rescue at sea. Ocean Engineering 38(17–18), 2157–2164 (2011)

[4] Abascal, A.J., Castanedo, S., Medina, R., Losada, I.J.: Stochastic Lagrangian modeling of surface drifters. Marine Pollution Bulletin 160, 111689 (2020)

[5] Röhrs, J., Christensen, K.H.: Drift in the uppermost part of the ocean: wave effects. Ocean Dynamics 71(2), 259–277 (2021)

[6] Zhang, H., Wang, Y., Liu, Z.: A review of maritime search and rescue optimization. Ocean Engineering 235, 109404 (2021)

[7] Wang, S., Zhang, J., Li, Y.: Maritime drifting trajectory prediction driven by deep learning and multi-source ocean data. Ocean Engineering 285, 115340 (2023)

[8] Golden, F.S.C., Tipton, M.J.: Essentials of Sea Survival. Human Kinetics, Champaign (2002)

[9] Tipton, M.J.: Cold water immersion and survival. Extreme Physiology & Medicine 8(1), 1–9 (2019)

[10] Liu, C., Wang, X., Chen, P.: Dynamic assessment of human survival state in cold water based on multi-parameter fusion. Reliability Engineering & System Safety 241, 109670 (2024)

[11] Dagestad, K.F., Röhrs, J., Breivik, Ø., Ådlandsvik, B.: OpenDrift v1.0: A generic framework for trajectory modeling. Geoscientific Model Development 11(4), 1405–1420 (2018)

[12] Zhao, M., Chen, Y., Liu, Z.: A scenario-driven digital twin simulation system for maritime emergency response. IEEE Transactions on Intelligent Transportation Systems 26(2), 1520–1533 (2025)

[13] Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y.: The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77(3), 437–471 (1996)

[14] Chen, C., Beardsley, R.C., Cowles, G.: An unstructured grid, finite-volume coastal ocean model (FVCOM). Oceanography 19(1), 78–89 (2006)

[15] Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)