Enhancing Marine Resource Management with AI-Enabled Decision-Making and Policy Solutions

Marine resource management encounters issues like overfishing, pollution, and ecological deterioration. It utilizes AI-driven models to tackle these challenges by using real-time data from maritime sensors, satellite photos, and historical records. Utilized methodologies include convolutional neural networks (CNNs) for ecosystem predictions, reinforcement learning for sustainable fishing limits, and regression models for pollution detection. Results indicate substantial enhancements: forecast accuracy attained 92.5%, overfishing reduction escalated from 12.3% to 35.6%, pollution detection speed advanced from 48 to 8 hours, and fleet efficiency climbed from 67.4% to 85.2%. AI-driven decision support systems (DSS) significantly improved policy implementation duration, decreasing it from 20 to 5 days. These findings illustrate the efficacy of AI in advancing sustainability and economic viability in managing marine resources. Subsequent efforts will enhance the models and augment the dataset for comprehensive ecological monitoring and policy-making incorporation.