Abstract
This study investigates the relationship between seabed structures and ship manoeuvrability by applying an anisotropic transformation to bathymetric data. The one of the selected study areas is the downstream section of the Mississippi River in Chalmette, Louisiana, a region characterized by meanders and a maximum depth of approximately 50 meters. The other study areas, which differ in their geomorphological and navigational characteristics, are the Upper New York Bay, Sacramento-San Joaquin River Delta and West Florida Escarpment. Bathymetric data, obtained from NOAA, were converted to fractional anisotropy maps using three wavelet kernels: Coiflet-1, Haar, and Daubechies-4. The anisotropy index was calculated per grid cell to capture directional dependencies in the seabed topography, which may influence ship movements. Statistical analysis, including descriptive statistics and non-parametric tests, was performed to compare the effectiveness of each wavelet kernel. The findings suggest that the Haar kernel is optimal for shallow areas, while the db4 kernel is most effective for detecting anisotropic patterns associated with heading deviations. This study demonstrates the importance of integrating seabed characteristics into predictive models for autonomous navigation, particularly in complex, shallow, and narrow waterways.
Keywords
Inland Waterways Bathymetric data Feature extraction Autonomous navigation Wavelet analysis
Project Number
(STB Project Code:100611), supported by Piri Reis University
References
- K. Bergman, O. Ljungqvist, J. Linder and D. Axehill, “A COLREGs-Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments,” arXiv preprint, vol. 12145, 2020.
- W. Mervade, “Effect of spatial trends on interpolation of river bathymetry,” Journal of Hydrology, vol. 371, no. 1-4, pp. 169-181, 2009.
- C. Jörges, C. Berkenbrink, H. Gottschalk and B. Stumpe, “Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry,” Ocean Engineering, vol. 271, no. 113699, 2023.
- C. Wang, M. Diao, S. Tong, L. Jiang, X. Tang and P. Jiang, “Numerical simulation study on ship manoeuvrability in mountainous rivers: Comprehensively considering effects of nonuniform flow, shallow water, narrow banks, winds and waves,” Ocean Engineering, vol. 306, no. 118109, 2024.
- H. Hess, “Seismic anisotropy of the uppermost mantle under oceans,” Nature, vol. 203, p. 629–631, 1964.
- M. Sormani, C. Redenbach, A. Särkkä and T. Rajala, “Second order analysis of geometric anisotropic point processes revisited,” Spatial Statistics, vol. 38, no. 100456, 2020.
- Z. Aslan, Z. N. Caglar and D. N. Yeniçeri, “Wavelet analyses of some atmospheric parameters at black sea region,” International Journal of Electronics Mechanical and Mechatronics Engineering, vol. 3, no. 1, pp. 419-426, 2013.
- M. Alam, G. Spadon, M. Etemad, L. Torgo and E. Milios, “Enhancing short- term vessel trajectory prediction with clustering for heterogeneous and multi-modal movement patterns,” Ocean Engineering, vol. 308, no. 118303, 2024.
- I. Slaughter, J. L. Charla, M. Siderius and J. Lipor, “Vessel trajectory prediction with recurrent neural networks: An evaluation of datasets, features, and architectures,” Journal of Ocean Engineering and Science, vol. 10, no. 2, p. 229–238, 2025.
- H. Li, H. Jiao and Z. Yang, “Ais data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods,” Transportation Research Part E: Logistics and Transportation Review, vol. 175, no. 103152, 2023.
- J. Bi, M. Gao, K. Bao, W. Zhang, X. Zhang and H. Cheng, “A CNNGRU-MHA method for ship trajectory prediction based on marine fusion data,” Ocean Engineering, vol. 310, no. 118701, 2024.
- P. Kumar and E. Foufoula-Georgiou, “Wavelet analysis of geophysical applications,” Reviews of geophysics, vol. 35, no. 4, pp. 385-412, 1997.
- M. J. Wilson, B. O'Connell, C. Brown, J. C. Guinan and A. J. Grehan, “Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope,” Marine Geodesy, vol. 30, no. 1, p. 3–35, 2007.
- A. Tommasi, B. Gibert and U. Seipold, “Anisotropy of thermal diffusivity in the upper mantle,” Nature, vol. 411, p. 783–786, 2001.
- İ. Öz, “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet,” TJST, vol. 19, no. 1, p. 279–294, 2024.
- F. Csillag and S. Kabos, “Wavelets, boundaries, and the spatial analysis of landscape pattern,” Écoscience, vol. 9, no. 2, pp. 177-190, 2002.
- R. M. Neupauer, K. L. Powell, X. Qi, D. H. Lee and D. A. Villhauer, “Characterization of permeability anisotropy using wavelet analysis,” Water Resources Research, vol. 42, no. W07419, 2006.
- Y. Guo, R. Zhao, Y. Zeng, Z. Shi and Q. Zhou, “Identifying scale-speci c controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform,” Catena, vol. 160, pp. 1-9, 2018.
- F. Bedoya-Maya, P. Shobayo, A. Nicolet, E. Christopoulou, I. Majoor, E. Hassel and T. Vanelslander, “Cargo consolidation in port-hinterland container transport: A spatial economic assessment for inland waterways,” Research in Transportation Business & Management, vol. 59, no. 101254, 2025.
- M. O'Neil, D. Taillie, B. Walsh, W. C. Dennison, E. K. Bone, D. J. Reid, R. Newton, D. L. Strayer, L. Boicourt, L. B. Birney, S. Janis, P. Malinowski and M. Fisher, “New York Harbor: Resilience in the face of four centuries of development,” Regional Studies in Marine Science, vol. 8, no. 2, pp. 274-286, 2016.
- “About the Watershed,” U.S. Environmental Protection Agency, [Online]. Available: https://www.epa.gov/sfbay-delta/about-watershed. [Accessed 31 05 2025].
- H. Robert, “Bathymetry of the Straits of Florida and Bahama Islands Part III. Southern Straits of Florida,” Deep Sea Research and Oceanographic Abstracts, vol. 12, no. 6, p. 1052, 1965.
- NOAA NCEI, “Bathymetric Data Viewer,” [Online]. Available: https://www.ncei.noaa.gov/maps/bathymetry/. [Accessed 05 01 2025].
- N. Ritter and M. Ruth, “The GeoTIFF data interchange standard for raster geospatial image data. U.S. Geological Survey,” [Online]. Available: https://www.remotesensing.org/geotiff/geotiff.html. [Accessed 1997].
- U.S. Department of Commerce, “Marine Cadastre AIS Data,” National Oceanic and Atmospheric Administration (NOAA), [Online]. Available: https://marinecadastre.gov. [Accessed 22 03 2025].
- E. Ross, C. Astrup, E. Bitner-Gregersen, N. Bunn, G. Feld, B. Gouldby, A. Huseby, Y. Liu, D. Randell, E. Vanem and P. Jonathan, “On environmental contours for marine and coastal design,” Ocean Engineering, vol. 195, no. 106194, 2020.
- T. Emmens, C. Amrit, A. Abdi and M. Ghosh, “The promises and perils of Automatic Identification System data,” Expert Systems with Applications, vol. 178, no. 114975, 2021.
- E. Tu, G. Zhang, L. Rachmawati, E. Rajabally and G. B. Huang, “Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey from Data to Methodology,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1559-1582, 2018.
- M. Chaudhary and A. Dhamija, “A brief study of various wavelet families and compression techniques,” Journal of Global Research in Computer Sciences, vol. 4, no. 4, p. 43–49, 2013.
- L. Debnath and F. A. Shah, Wavelet transforms and their applications, Birkhauser, 2015.
- D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613 - 627, 2002.
- M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Transactions on Image Processing, vol. 4, no. 11, p. 1549–1560, 1995.
- A. Akl and J. Iskandar, “Second-moment matrix adaptation for local orientation estimation,” in International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, 2016.
- Z. Liu, W. Qi, S. Zhou, W. Zhang, C. Jiang, Y. Jie, C. Li, Y. Guo and J. Guo, “Hybrid deep learning models for ship trajectory prediction in complex scenarios based on ais data,” Applied Ocean Research, vol. 153, no. 104231, 2024.
Abstract
This study investigates the relationship between seabed structures and ship manoeuvrability by applying an anisotropic transformation to bathymetric data. The one of the selected study areas is the downstream section of the Mississippi River in Chalmette, Louisiana, a region characterized by meanders and a maximum depth of approximately 50 meters. The other study areas, which differ in their geomorphological and navigational characteristics, are the Upper New York Bay, Sacramento-San Joaquin River Delta and West Florida Escarpment. Bathymetric data, obtained from NOAA, were converted to fractional anisotropy maps using three wavelet kernels: Coiflet-1, Haar, and Daubechies-4. The anisotropy index was calculated per grid cell to capture directional dependencies in the seabed topography, which may influence ship movements. Statistical analysis, including descriptive statistics and non-parametric tests, was performed to compare the effectiveness of each wavelet kernel. The findings suggest that the Haar kernel is optimal for shallow areas, while the db4 kernel is most effective for detecting anisotropic patterns associated with heading deviations. This study demonstrates the importance of integrating seabed characteristics into predictive models for autonomous navigation, particularly in complex, shallow, and narrow waterways.
Keywords
Inland Waterways Bathymetric data Feature extraction Autonomous navigation Wavelet analysis
Ethical Statement
It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography
Supporting Institution
Piri Reis University
Project Number
(STB Project Code:100611), supported by Piri Reis University
References
- K. Bergman, O. Ljungqvist, J. Linder and D. Axehill, “A COLREGs-Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments,” arXiv preprint, vol. 12145, 2020.
- W. Mervade, “Effect of spatial trends on interpolation of river bathymetry,” Journal of Hydrology, vol. 371, no. 1-4, pp. 169-181, 2009.
- C. Jörges, C. Berkenbrink, H. Gottschalk and B. Stumpe, “Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry,” Ocean Engineering, vol. 271, no. 113699, 2023.
- C. Wang, M. Diao, S. Tong, L. Jiang, X. Tang and P. Jiang, “Numerical simulation study on ship manoeuvrability in mountainous rivers: Comprehensively considering effects of nonuniform flow, shallow water, narrow banks, winds and waves,” Ocean Engineering, vol. 306, no. 118109, 2024.
- H. Hess, “Seismic anisotropy of the uppermost mantle under oceans,” Nature, vol. 203, p. 629–631, 1964.
- M. Sormani, C. Redenbach, A. Särkkä and T. Rajala, “Second order analysis of geometric anisotropic point processes revisited,” Spatial Statistics, vol. 38, no. 100456, 2020.
- Z. Aslan, Z. N. Caglar and D. N. Yeniçeri, “Wavelet analyses of some atmospheric parameters at black sea region,” International Journal of Electronics Mechanical and Mechatronics Engineering, vol. 3, no. 1, pp. 419-426, 2013.
- M. Alam, G. Spadon, M. Etemad, L. Torgo and E. Milios, “Enhancing short- term vessel trajectory prediction with clustering for heterogeneous and multi-modal movement patterns,” Ocean Engineering, vol. 308, no. 118303, 2024.
- I. Slaughter, J. L. Charla, M. Siderius and J. Lipor, “Vessel trajectory prediction with recurrent neural networks: An evaluation of datasets, features, and architectures,” Journal of Ocean Engineering and Science, vol. 10, no. 2, p. 229–238, 2025.
- H. Li, H. Jiao and Z. Yang, “Ais data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods,” Transportation Research Part E: Logistics and Transportation Review, vol. 175, no. 103152, 2023.
- J. Bi, M. Gao, K. Bao, W. Zhang, X. Zhang and H. Cheng, “A CNNGRU-MHA method for ship trajectory prediction based on marine fusion data,” Ocean Engineering, vol. 310, no. 118701, 2024.
- P. Kumar and E. Foufoula-Georgiou, “Wavelet analysis of geophysical applications,” Reviews of geophysics, vol. 35, no. 4, pp. 385-412, 1997.
- M. J. Wilson, B. O'Connell, C. Brown, J. C. Guinan and A. J. Grehan, “Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope,” Marine Geodesy, vol. 30, no. 1, p. 3–35, 2007.
- A. Tommasi, B. Gibert and U. Seipold, “Anisotropy of thermal diffusivity in the upper mantle,” Nature, vol. 411, p. 783–786, 2001.
- İ. Öz, “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet,” TJST, vol. 19, no. 1, p. 279–294, 2024.
- F. Csillag and S. Kabos, “Wavelets, boundaries, and the spatial analysis of landscape pattern,” Écoscience, vol. 9, no. 2, pp. 177-190, 2002.
- R. M. Neupauer, K. L. Powell, X. Qi, D. H. Lee and D. A. Villhauer, “Characterization of permeability anisotropy using wavelet analysis,” Water Resources Research, vol. 42, no. W07419, 2006.
- Y. Guo, R. Zhao, Y. Zeng, Z. Shi and Q. Zhou, “Identifying scale-speci c controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform,” Catena, vol. 160, pp. 1-9, 2018.
- F. Bedoya-Maya, P. Shobayo, A. Nicolet, E. Christopoulou, I. Majoor, E. Hassel and T. Vanelslander, “Cargo consolidation in port-hinterland container transport: A spatial economic assessment for inland waterways,” Research in Transportation Business & Management, vol. 59, no. 101254, 2025.
- M. O'Neil, D. Taillie, B. Walsh, W. C. Dennison, E. K. Bone, D. J. Reid, R. Newton, D. L. Strayer, L. Boicourt, L. B. Birney, S. Janis, P. Malinowski and M. Fisher, “New York Harbor: Resilience in the face of four centuries of development,” Regional Studies in Marine Science, vol. 8, no. 2, pp. 274-286, 2016.
- “About the Watershed,” U.S. Environmental Protection Agency, [Online]. Available: https://www.epa.gov/sfbay-delta/about-watershed. [Accessed 31 05 2025].
- H. Robert, “Bathymetry of the Straits of Florida and Bahama Islands Part III. Southern Straits of Florida,” Deep Sea Research and Oceanographic Abstracts, vol. 12, no. 6, p. 1052, 1965.
- NOAA NCEI, “Bathymetric Data Viewer,” [Online]. Available: https://www.ncei.noaa.gov/maps/bathymetry/. [Accessed 05 01 2025].
- N. Ritter and M. Ruth, “The GeoTIFF data interchange standard for raster geospatial image data. U.S. Geological Survey,” [Online]. Available: https://www.remotesensing.org/geotiff/geotiff.html. [Accessed 1997].
- U.S. Department of Commerce, “Marine Cadastre AIS Data,” National Oceanic and Atmospheric Administration (NOAA), [Online]. Available: https://marinecadastre.gov. [Accessed 22 03 2025].
- E. Ross, C. Astrup, E. Bitner-Gregersen, N. Bunn, G. Feld, B. Gouldby, A. Huseby, Y. Liu, D. Randell, E. Vanem and P. Jonathan, “On environmental contours for marine and coastal design,” Ocean Engineering, vol. 195, no. 106194, 2020.
- T. Emmens, C. Amrit, A. Abdi and M. Ghosh, “The promises and perils of Automatic Identification System data,” Expert Systems with Applications, vol. 178, no. 114975, 2021.
- E. Tu, G. Zhang, L. Rachmawati, E. Rajabally and G. B. Huang, “Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey from Data to Methodology,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1559-1582, 2018.
- M. Chaudhary and A. Dhamija, “A brief study of various wavelet families and compression techniques,” Journal of Global Research in Computer Sciences, vol. 4, no. 4, p. 43–49, 2013.
- L. Debnath and F. A. Shah, Wavelet transforms and their applications, Birkhauser, 2015.
- D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613 - 627, 2002.
- M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Transactions on Image Processing, vol. 4, no. 11, p. 1549–1560, 1995.
- A. Akl and J. Iskandar, “Second-moment matrix adaptation for local orientation estimation,” in International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, 2016.
- Z. Liu, W. Qi, S. Zhou, W. Zhang, C. Jiang, Y. Jie, C. Li, Y. Guo and J. Guo, “Hybrid deep learning models for ship trajectory prediction in complex scenarios based on ais data,” Applied Ocean Research, vol. 153, no. 104231, 2024.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Article |
| Authors | |
| Project Number | (STB Project Code:100611), supported by Piri Reis University |
| Early Pub Date | September 26, 2025 |
| Publication Date | September 30, 2025 |
| Submission Date | March 25, 2025 |
| Acceptance Date | July 24, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 3 |
Cite
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