saucis Sakarya University Journal of Computer and Information Sciences 2636-8129 Sakarya University 10.35377/saucis.8.94717.1665521 Computer Software Bilgisayar Yazılımı Comparative Analysis of Wavelet-Based Anisotropy Index Calculation for Bathymetric Data Comparative Analysis of Wavelet-Based Anisotropy Index Calculation for Bathymetric Data https://orcid.org/0000-0002-6339-8736 Er Füsun PIRI REIS UNIVERSITY, FACULTY OF ENGINEERING https://orcid.org/0000-0003-0647-5931 Ersoy Şengül PİRİ REİS ÜNİVERSİTESİ, FEN-EDEBİYAT FAKÜLTESİ https://orcid.org/0000-0002-2313-4525 Yalman Yıldıray PIRI REIS UNIVERSITY, FACULTY OF ENGINEERING 09 30 2025 8 3 470 483 03 25 2025 07 24 2025 Copyright © 2018, Sakarya University Journal of Computer and Information Sciences 2018 Sakarya University Journal of Computer and Information Sciences

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.

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.

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