The reef ecosystem plays a vital role as a habitat for fish species with limited swimming capabilities, serving not only as a sanctuary and food source but also influencing their behavioral tendencies. Understanding the intricate mechanism through which fish adeptly navigate the moving targets within reef environments within complex water flow, all while evading obstacles and maintaining stable postures, has remained a challenging and prominent subject in the realms of fish behavior, ecology, and biomimetics alike. An integrated simulation framework is used to investigate fish predation problems within intricate environments, combining deep reinforcement learning algorithms (DRL) with high-precision fluid-structure interaction numerical methods-immersed boundary lattice Boltzmann method (lB-LBM). The Soft Actor-Critic (SAC) algorithm is used to improve the intelligent fish’s capacity for random exploration, tackling the multi-objective sparse reward challenge inherent in real-world scenarios. Additionally, a reward shaping method tailored to its action purposes has been developed, capable of capturing outcomes and trend characteristics effectively. The convergence and robustness advantages of the method elucidated in this paper are showcased through two case studies: one addressing fish capturing randomly moving targets in hydrostatic flow field, and the other focusing on fish counter-current foraging in reef environments to capture drifting food. A comprehensive analysis was conducted of the influence and significance of various reward types on the decision-making processes of intelligent fish within intricate environments.

With the development of sustainable inland waterway transportation, maritime safety has become a matter of wide concern. Causation analysis of ship accidents is known as an important prerequisite for achieving waterborne transportation safety. A novel AcciMap–graph convolutional network (GCN)-based causation analysis model has been proposed for maritime traffic accidents in the main Yangtze River waterway. Mutual information, a grey wolf optimizer (GWO), a dropout layer and weakly supervised learning are introduced to obtain causal chains and the causal importance scores of accident causes. The results indicate that, compared with baseline models (e.g., GIN, GAT, APPNP, and GraphSAGE), the AcciMap–GCN model achieved the lowest values for all performance metrics, with an MAE of 0.0820, an RMSE of 0.1694, and a training convergence epoch of 74. The model robustness was comprehensively investigated through ablation experiments. Based on the causal importance scores of accident causes, maritime safety administration strategies are systematically proposed. The present study provides a novel perspective for analyzing the causal relationships of maritime accidents and shares useful insights into sustainable waterway transportation.