A comprehensive model for assessing the risk of loss of navigational characteristics in a navigable channel
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Published:
Jul 1, 2026
Keywords:
fleet operation, maritime transport, navigation, navigational characteristics, risk, ship control, mathematical modeling, vessel operations manage-ment, decision support systems
Main Article Content
Abstract
The operational efficiency of navigable channels in delta systems is largely determined by their ability to maintain standard navigational characteristics under the constant influence of sedimentation processes, hydrometeorological factors, and morphodynamic changes in the channel bed. A deterioration in navigational parameters leads to restrictions on vessel draft, a reduction in the waterway’s capacity, and increased maintenance costs. This paper presents a comprehensive model for assessing the risk of loss of navigational characteristics in a navigable channel. The proposed model is based on the integration of factor analysis, a probabilistic approach, fuzzy logic, and dynamic risk modeling. An integrated risk index has been introduced that accounts for the influence of morphodynamic, hydrological, sedimentation, and operational factors. A decision-making criterion has been developed regarding the necessity of dredging operations based on an acceptable risk level. The results obtained can be used in the development of decision support systems for managing the operational condition of navigation channels and planning dredging operations.
Article Details
How to Cite
Naumenko, A. (2026). A comprehensive model for assessing the risk of loss of navigational characteristics in a navigable channel. Herald of the Odesa National Maritime University, (80), 221-239. https://doi.org/10.47049/2226-1893-2026-2-221-239
Section
Ensuring the safety of navigation
References
1. Wang, Z., Mu, D., Zhao, Y., Liang, X., & Fan, Y. (2026). Dynamics aware spatio-temporal path planning for USV in reef–island waters under time-varying environmental disturbances. Ocean Engineering, 362, 126360. https://doi.org/10.1016/j.oceaneng.2026.126360.
2. Zhang, W., Ma, X., & Zhang, Y. (2025). Research on navigation risk assessment index system of intelligent ships. Ocean Engineering, 322, 120435. https://doi.org/10.1016/j.oceaneng.2025.120435.
3. Guan, H., Xu, Y., Li, L., Deng, J., Ma, W., Gong, X., & Zhang, T. (2025). A novel attention mechanism-based multi-branch long short-term memory network model for mental workload recognition of crew in tunnel navigation. Ocean Engineering, 339, 122141. https://doi.org/10.1016/j.oceaneng.2025.122141.
4. Wang, P., Zhou, J., Zheng, K., Jin, X., Chan, N. W., Tan, M. L., Lin, X., Wu, J., Jim, C.Y., Johnson, V.C., & Zhang, F. (2025). Impacts of nearshore photovoltaic installation on mariculture and navigation based on the MIKE21 hydrodynamic model. Journal of Sea Research, 209, 102651. https://doi.org/10.1016/j.seares.2025.102651.
5. Medina, R., Marino-Tapia, I., Osorio, A., Davidson, M., & Martin, F. (2007). Management of dynamic navigational channels using video techniques. Coastal Engineering, 54(6-7), 523-537. https://doi.org/10.1016/j.coastaleng.2007.01.008
6. Liu, X., He, Y., Zhang, K., Mou, J., Zhang, K., & Zhao, X. (2025). Research on the decision-making method for autonomous navigation for the ocean-going ship in the ships’ routeing waters. Ocean Engineering, 323, 120641. https://doi.org/10.1016/j.oceaneng.2025.120641.
7. O'Reilly, G. J., Shahnazaryan, D., Dubini, P., Brunesi, E., Rosti, A., Dacarro, F., Gotti, A., Silvestri, D., Mascetti, S., Ducci, M., Ciucci, M., & Marino, A. (2023). Risk-aware navigation in industrial plants at risk of NaTech accidents. International Journal of Disaster Risk Reduction, 88, 103620. https://doi.org/10.1016/j.ijdrr.2023.103620.
8. Zhang, W., Zhang, Y., & Zhang, C. (2024). Navigation risk assessment of intelligent ships based on DS-Fuzzy weighted distance Bayesian network. Ocean Engineering, 313, 119452. https://doi.org/10.1016/j.oceaneng.2024.119452
9. Chen, X., Zhou, H., Li, M., Grifoll, M., Zhou, Y., Liu, J., & Zheng, P. (2026). Quantifying ship-bridge collision risk through extreme value theory: A new methodological framework. Ocean Engineering, 347, 124023. https://doi.org/10.1016/j.oceaneng.2025.124023.
10. Li, W., Liao, M., & Zou, W. (2025). A progressive segmentation network for navigable areas with semantic–spatial information flow. Expert Systems With Applications, 261, 125465. https://doi.org/10.1016/j.eswa.2024.125465
11. He, Z., Li, S., Wang, S., & Zhuang, Z. (2021). Mining navigable water current information from ship-based big automated identification system data. Ocean Engineering, 242, 110076. https://doi.org/10.1016/j.oceaneng.2021.110076.
12. Zuo, W., Liu, Z., Zhuang, Y., Wan, C., & Shu, Y. (2026). A data-driven approach for determining navigation waters influenced by bridges – A framework and a case study. Ocean Engineering, 352, 124509. https://doi.org/10.1016/j.oceaneng.2026.124509.
13. Huang, C., Wang, S., Zheng, J., Chen, X., & Zhou, J. (2025). Ship formation scheduling optimization for the multi-compound channel with deterministic and uncertain arrivals. Ocean Engineering, 342, 122815. https://doi.org/10.1016/j.oceaneng.2025.122815.
14. Lin, M., Bakhsheshian, J., Strickland, B., Rennert, R.C., Chu, R.M., Chaichana, K.L., & Zada, G. (2019). Exoscopic resection of atrial intraventricular meningiomas using a navigation-assisted channel-based trans-sulcal approach: Case series and literature review. Journal of Clinical Neuroscience, 71, 58-65. https://doi.org/10.1016/j.jocn.2019.10.017.
15. Brenna, A., Bizzi, S., & Surian, N. (2023). How multiple anthropic pressures may lead to unplanned channel patterns: Insights from the evolutionary trajectory of the Po River (Italy). CATENA, 234, 107598. https://doi.org/10.1016/j.catena.2023.107598.
2. Zhang, W., Ma, X., & Zhang, Y. (2025). Research on navigation risk assessment index system of intelligent ships. Ocean Engineering, 322, 120435. https://doi.org/10.1016/j.oceaneng.2025.120435.
3. Guan, H., Xu, Y., Li, L., Deng, J., Ma, W., Gong, X., & Zhang, T. (2025). A novel attention mechanism-based multi-branch long short-term memory network model for mental workload recognition of crew in tunnel navigation. Ocean Engineering, 339, 122141. https://doi.org/10.1016/j.oceaneng.2025.122141.
4. Wang, P., Zhou, J., Zheng, K., Jin, X., Chan, N. W., Tan, M. L., Lin, X., Wu, J., Jim, C.Y., Johnson, V.C., & Zhang, F. (2025). Impacts of nearshore photovoltaic installation on mariculture and navigation based on the MIKE21 hydrodynamic model. Journal of Sea Research, 209, 102651. https://doi.org/10.1016/j.seares.2025.102651.
5. Medina, R., Marino-Tapia, I., Osorio, A., Davidson, M., & Martin, F. (2007). Management of dynamic navigational channels using video techniques. Coastal Engineering, 54(6-7), 523-537. https://doi.org/10.1016/j.coastaleng.2007.01.008
6. Liu, X., He, Y., Zhang, K., Mou, J., Zhang, K., & Zhao, X. (2025). Research on the decision-making method for autonomous navigation for the ocean-going ship in the ships’ routeing waters. Ocean Engineering, 323, 120641. https://doi.org/10.1016/j.oceaneng.2025.120641.
7. O'Reilly, G. J., Shahnazaryan, D., Dubini, P., Brunesi, E., Rosti, A., Dacarro, F., Gotti, A., Silvestri, D., Mascetti, S., Ducci, M., Ciucci, M., & Marino, A. (2023). Risk-aware navigation in industrial plants at risk of NaTech accidents. International Journal of Disaster Risk Reduction, 88, 103620. https://doi.org/10.1016/j.ijdrr.2023.103620.
8. Zhang, W., Zhang, Y., & Zhang, C. (2024). Navigation risk assessment of intelligent ships based on DS-Fuzzy weighted distance Bayesian network. Ocean Engineering, 313, 119452. https://doi.org/10.1016/j.oceaneng.2024.119452
9. Chen, X., Zhou, H., Li, M., Grifoll, M., Zhou, Y., Liu, J., & Zheng, P. (2026). Quantifying ship-bridge collision risk through extreme value theory: A new methodological framework. Ocean Engineering, 347, 124023. https://doi.org/10.1016/j.oceaneng.2025.124023.
10. Li, W., Liao, M., & Zou, W. (2025). A progressive segmentation network for navigable areas with semantic–spatial information flow. Expert Systems With Applications, 261, 125465. https://doi.org/10.1016/j.eswa.2024.125465
11. He, Z., Li, S., Wang, S., & Zhuang, Z. (2021). Mining navigable water current information from ship-based big automated identification system data. Ocean Engineering, 242, 110076. https://doi.org/10.1016/j.oceaneng.2021.110076.
12. Zuo, W., Liu, Z., Zhuang, Y., Wan, C., & Shu, Y. (2026). A data-driven approach for determining navigation waters influenced by bridges – A framework and a case study. Ocean Engineering, 352, 124509. https://doi.org/10.1016/j.oceaneng.2026.124509.
13. Huang, C., Wang, S., Zheng, J., Chen, X., & Zhou, J. (2025). Ship formation scheduling optimization for the multi-compound channel with deterministic and uncertain arrivals. Ocean Engineering, 342, 122815. https://doi.org/10.1016/j.oceaneng.2025.122815.
14. Lin, M., Bakhsheshian, J., Strickland, B., Rennert, R.C., Chu, R.M., Chaichana, K.L., & Zada, G. (2019). Exoscopic resection of atrial intraventricular meningiomas using a navigation-assisted channel-based trans-sulcal approach: Case series and literature review. Journal of Clinical Neuroscience, 71, 58-65. https://doi.org/10.1016/j.jocn.2019.10.017.
15. Brenna, A., Bizzi, S., & Surian, N. (2023). How multiple anthropic pressures may lead to unplanned channel patterns: Insights from the evolutionary trajectory of the Po River (Italy). CATENA, 234, 107598. https://doi.org/10.1016/j.catena.2023.107598.