Despite being a minor probability event, marine accidents can cause serious consequences such as casualties, environmental damage, and even massive economic losses. As an important part of the marine traffic safety system, the evaluation of navigation safety in coastal waters is of great significance to ensure the safety of ship navigation. In order to objectively evaluate the safety of navigation, this paper proposes an irregular grid division method that combines influencing factors including seabed topography, ship traffic flow, electronic charts, and marine meteorology. In this work, the navigable boundary was extracted by spatial analysis algorithms of the slope calculation. Based on the alpha-shape algorithm and the Voronoi diagram, the constrained Delaunay triangulation was used to extract the inner and outer boundaries of the ship′s navigation area, and the time-varying factors of marine hydrology and meteorology were integrated to form a dynamic irregular grid. The navigation safety evaluation indicators in coastal waters were divided into five risk levels, namely lower, low, medium, high, and higher. Then, the entropy weight theory was used to calculate the weight of the evaluation index. Finally, a safety evaluation model was constructed to evaluate the risk of navigation safety in coastal waters. Herein, taking the coastal waters of Lianyungang Port as a demonstration area, this paper divided the dynamic irregular grid and conducted the navigation safety analysis and evaluation based on the grid. The experimental results show that our method fully considers the influence of objective factors and the uncertainty of safety evaluation indicators and has favorable adaptability to the evaluation of navigation safety in coastal waters.

The Changzhou ship lock is approaching its capacity limit. In order to quantitatively analyze the influencing factors that restrict the capacity of the Changzhou ship lock, this study proposes an influencing factor analysis method based on principal component analysis (PCA). This method estimates the confidence interval of ship passing time by fitting a lognormal distribution curve, eliminates redundancy in navigability data by combining the hydrological data and cargo load data, and quantitatively analyzes the influencing factors of ship lock capacity under saturated operating conditions. The results show that the influencing factors of Changzhou ship lock capacity are classified according to their influence contribution rate as minimum water depth above the lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. The research results can provide a theoretical basis for improving the ship lock capacity and have application value for lock scheduling management.

Approach channels are affected by sedimentation and scour, resulting in continuous changes in underwater topography. Such processes tend to generate shallow spots and inadequate navigable dimensions, posing safety hazards that undermine both waterway resilience and navigation capacity. To address these issues, this paper proposes a multi-level grid-based spatiotemporal indexing method for bathymetric data, aiming to support resilience-oriented management by improving the effectiveness of bathymetric data management. First, a channel-segment-section partitioning strategy is designed to construct hierarchical progressive grids for the efficient organization of massive bathymetric data. Second, a multi-dimensional spatiotemporal integrated query method is developed to meet diverse analytical and retrieval requirements. Third, a digital depth model (DDM) construction method is introduced that integrates boundary-constrained terrain reconstruction with efficient mesh optimization, enabling underwater terrain representation that adapts to the elongated and irregular morphology of approach channels. The contribution of this work lies not in proposing new individual algorithms but in the tailored integration of these techniques to address the specific challenges of approach-channel bathymetric data. Experimental results demonstrate that the proposed method achieves high construction efficiency across different storage and query schemes. The method enhances the retrieval and analytical capabilities of bathymetric data in representative application scenarios, such as shallow spot identification, critical section analysis, dredging analysis, and erosion–deposition evolution. Consequently, these improvements provide technical support for resilience-oriented channel management and ensure navigational safety.

As an important component of waters constituent that affects ocean color and the underwater ecological environment, the accurate assessment of Chromophoric Dissolved Organic Matter (CDOM) is crucial for observing the continuous changes in the marine ecosystem. However, remote sensing estimation of CDOM remains challenging for both coastal and oceanic waters due to its weak optical signals and complex optical conditions. Therefore, the development of efficient, practical, and robust models for estimating the CDOM absorption coefficient in both coastal and oceanic waters remains an active research focus. This study presents a novel algorithm (denoted as DQAAG) that incorporates ultraviolet bands into the inversion model. The design leverages the distinct spectral absorption characteristics of phytoplankton versus detrital particles in the ultraviolet (UV) region, enabling improved discrimination of water color parameters. Furthermore, the algorithm replaces empirical formulas commonly used in semi-analytical approaches with an artificial intelligence model (deep learning) to achieve enhanced inversion accuracy. Using IOCCG hyperspectral simulation data and NOMAD dataset to evaluates Shanmugam (2011) (S2011), Aurin et al. (2018) (A2018), Zhu et al. (2011) (QAA-CDOM), DQAAG, the results indicate that the a_g(443) derived from the DQAAG exhibit good agreement with the validation data, with root mean square deviation (RMSD) < 0.3 m⁻¹, mean absolute relative difference (MARD) < 0.30, mean bias (bias) < 0.028 m⁻¹, coefficient of determination (R²) > 0.78. The DQAAG algorithm was applied to SeaWiFS remote sensing data, and validation was performed through match-up analysis with the NOMAD dataset. The results show the RMSD = 0.14 m⁻¹, MARD = 0.39, and R² = 0.62. Through a sensitivity analysis of the algorithm, the study reveals that R_rs(670) and R_rs(380) exhibit more significant characteristics. These results demonstrate that UV bands play a crucial role in enhancing the retrieval accuracy of ocean color parameters. In addition, DQAAG, which integrates semi-analytical algorithms with artificial intelligence, presents an encouraging approach for processing ocean color imagery to retrieve a_g(443).