Hybrid Machine Learning Approach For Fishermen Safety And Communication In Marine Environments

The study proposes an intelligent and reliable hybrid framework for enhancing fishermen safety and communication in marine environments using machine learning and electromagnetic water networks. Fishing activities in deep-sea regions involve significant risks due to unpredictable weather conditions, accidental border crossings, and limited communication facilities. Traditional monitoring systems rely heavily on manual observation and basic GPS tracking, which are often inefficient in handling real-time emergencies and dynamic ocean conditions. Additionally, the lack of continuous monitoring and predictive capabilities increases the vulnerability of fishermen to accidents and environmental hazards.To address these challenges, the proposed system integrates real-time data acquisition from multiple sources, including GPS tracking, environmental sensors, and electromagnetic sensors, to ensure continuous monitoring of marine conditions. The system employs machine learning techniques such as anomaly detection algorithms to identify abnormal vessel behavior, including sudden stops, unusual movements, and route deviations that may indicate distress situations. Furthermore, time-series data collected from sensors is analyzed using advanced deep learning techniques to predict environmental changes such as weather fluctuations and sea conditions.The model is trained and evaluated to accurately detect potential risks and provide early warning alerts, thereby enabling proactive decision-making. The proposed multi-layer framework enhances system performance by combining real-time monitoring, anomaly detection, and predictive analysis. This integrated approach improves communication between fishermen and coastal authorities through wireless technologies, ensuring timely response during emergencies.The system significantly enhances maritime safety, reduces the risk of accidents, and improves operational efficiency. By leveraging machine learning and real-time data processing, the proposed solution provides a scalable, efficient, and intelligent framework for ensuring the safety and security of fishermen in modern maritime environments.