Research on Real-time Monitoring and Risk Assessment System for Ocean Heavy Metal Pollution Driven by Deep Learning

Le résumé fourni par la source

Aiming at the risk assessment problem of ocean heavy metal pollution, a deep learning-based risk assessment system was constructed. A three-layer evaluation index system including pollutant hazard degree, environmental sensitivity, and exposure risk was established, and a graph neural network risk prediction model was developed. The system achieved a prediction accuracy of 91.2% in the validation of 2,876 monitoring point data. A practical application case showed that the system successfully warned of three potential pollution events in a coastal industrial park, 48 hours in advance, providing important decision-making support for ocean environmental management.

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