We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review
AI summary Read the abstract
This systematic literature review analyzed AI applications for marine pollution management from Web of Science and Scopus databases, finding that 57% target monitoring, 24% management, and 19% prediction. The review identifies oil spill detection, water quality monitoring, and plastic identification as the three major application areas where AI offers the greatest capabilities.
Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.
More Papers Like This
The Role of Artificial Intelligence in Marine Pollution: A Review
AI summary Read the abstract
This review pulls together current research on how artificial intelligence is being used to track ocean pollution, including oil spills, trash, and microplastics — tiny plastic particles that end up in seafood and drinking water. Since these pollutants can make their way into the food we eat, faster and more accurate AI-powered detection could help catch problems earlier and inform cleanup decisions, though the technology is still being tested and isn't yet solving the problem on its own.
Smart Ocean: A Comprehensive Review of Artificial Intelligence-Driven Ocean Monitoring, Forecasting, Exploration and Conservation
AI summary Read the abstract
A systematic review of AI applications in ocean science found that machine learning tools can detect illegal fishing with 99% precision and map coral reefs with 80% accuracy, though geographic data bias and lack of model interpretability limit real-world deployment. These AI monitoring capabilities are relevant to microplastic research as the same ocean surveillance infrastructure can be extended to detect and track marine plastic pollution distribution and transport.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
AI summary Read the abstract
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
Leveraging Artificial Intelligence for Plastic Waste Management in Coastal Waters: A Drone-Based Approach for Identification and Hotspot Detection
AI summary Read the abstract
A machine learning model trained on drone footage achieved 89.4% accuracy in classifying plastic debris types in coastal waters and identifying hotspot areas for targeted cleanup operations. The AI-driven approach offers faster and more scalable monitoring than traditional methods, though detection of small microplastics and environmental variability remain challenges.
AI-enhanced real-time monitoring of marine pollution: part 1-A state-of-the-art and scoping review
AI summary Read the abstract
A review of 53 studies on AI applications in marine pollution monitoring finds that models like Random Forest, U-Net, and YOLO achieve high accuracy in detecting oil spills and marine litter from remote sensing data. Key challenges remain, including limited training datasets and real-time processing constraints that must be addressed for scalable deployment.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.