Enhanced Fishermen Safety and Communication Using Machine Learning in Electromagnetic Water Networks

The study investigates machine learning integration with electromagnetic-based water wireless networks to enhance maritime safety and fisherman communication systems. Fishermen in international water regions face severe threats when unpredictable weather occurs or accidental border intrusions take the research focuses on developing an innovative system that relies on GPS tracking together with machine learning to stop boundary infringements while delivering instant safety warnings. Proactive boundary and dangerous situation predictions develop through machine learning algorithms that analyse GPS and environmental historical data. Time-series analytics enables the system to forecast unfavourable weather conditions which triggers early warnings for fishermen. An anomaly detection model conducts pattern discovery to detect abrupt stops or deviations that indicate distress so it automatically triggers an SOS alarm. The sustainable solar-powered system functions to enhance maritime safety through strongly managed communications along with protective measures. The study presents a complete approach for maritime protection and safer fishing through the combination of machine learning analysis with electromagnetic-based communication networks.