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Describing the structural complexity of seabeds is of primary importance for a number of geomorphological, hydrodynamical and ecological issues. Aiming to bring a decisive insight on the long-term development of a unified view, the present study reports on a comparative multi-site analysis of high resolution topography surveys in rough nearshore environments. The nine study sites have been selected to cover a wide variety of topographical features, including rocky and coral seabeds. The topography data has been processed to separate roughness and bathymetry-related terrain features, allowing to perform a comprehensive spectral and statistical analysis of each site. A series of roughness metrics have been tested to identify the most relevant estimators of the bottom roughness at each site. The spectral analysis highlights the systematic presence of a self-affine range of variable extension and spectral slope. The standard deviation of the seabed elevation varies from 0.04 to 0.77 m. The statistical and multi-scale analysis performed on the whole set of roughness metrics allows to identify connection between metrics and therefore to propose a reduced set of relevant roughness estimators. A more general emphasis is placed on the need to properly define a unified framework when reconstructing roughness statistics and bathymetry from fine seabed topographical data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sous et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After describing compositional data imbalance, this paper proposes an adaptation of the original Synthetic Minority Oversampling TEchnique (SMOTE) to deal with compositional data imbalance. The new approach, called SMOTE for Compositional Data (SMOTE-CD), generates synthetic examples by computing a linear combination of selected existing data points, using compositional data operations. The performance of the SMOTE-CD is tested with three different regressors (Gradient Boosting tree, Neural Networks, Dirichlet regressor) applied to two real datasets and to synthetic generated data, and the performance is evaluated using accuracy, cross-entropy, F1-score, R2 score and RMSE. The results show improvements across all metrics, but the impact of oversampling on performance varies depending on the model and the data. In some cases, oversampling may lead to a decrease in performance for the majority class. However, for the real data, the best performance across all models is achieved when oversampling is used. Notably, the F1-score is consistently increased with oversampling. Unlike the original technique, the performance is not improved when combining oversampling of the minority classes and undersampling of the majority class. The Python package smote-cd implements the method and is available online., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Nguyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)