Date of Publication :15th March 2025

Abstract: Oceanographic analysis is a crucial aspect of understanding marine ecosystems and improving navigation safety. Historically, oceanographic studies relied on manual observations and simple instruments like buoys, drifters, and sonar. However, these methods faced limitations like human intervention, limited spatial cover, and real-time processing constraints. Advances in artificial intelligence (AI), machine learning (ML), and remote sensing have transformed ocean monitoring. Advanced techniques like AUVs powered by AI, satellite oceanographic analysis, and Internet of Things sensor networks have improved the accuracy of ocean parameter predictions. Machine learning algorithms like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have significantly improved the accuracy of ocean parameter predictions. The proposed CNN -LSTM hybrid model focuses on spatial and temporal data for real-time maritime decision-making. Emerging trends like quantum sensing, blockchain-based ocean data security, and AI-based weather forecasting are exploring the future of ocean monitoring, making it more autonomous, precise, and green for safer and smarter shipping.

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