Intelligent Oceans

The complexity and dynamism of marine systems present significant challenges to traditional Marine Spatial Planning (MSP), particularly in engineering scalable, adaptive, and high-resolution decision frameworks. This chapter introduces an advanced, AI-driven systems architecture designed for sustainable MSP, integrating real-time data streams, machine learning (ML), deep learning (DL), and multi-objective optimization algorithms. The proposed hybrid framework features modular components for data ingestion, spatiotemporal prediction, scenario simulation, and decision support. These include LSTM and CNN networks for trajectory and environmental forecasting, GANs for data augmentation, and reinforcement learning (RL) for adaptive zoning and resource allocation. Developing strategies to transition prototypes into effective applications that enhance oceanic operations and the blue economy. This work introduces a transformative approach to “Intelligent Oceans,” integrating AI and systems engineering for enhanced decision-support systems.