中国P站

Abstract

Marine pollution has emerged as a significant environmental challenge, with millions of tons of waste flowing into oceans each year, causing disruptions in marine ecosystems. Traditional methods for monitoring and addressing this pollution are inadequate in dealing with its ineffable complexity. Researchers are thoroughly investigating the potential of artificial intelligence (AI) as a revolutionary tool for tracking and reducing ocean pollution. This literature review explores the application of cutting-edge technology in tracking and mitigating marine pollution, such as plastic waste, oil spills, and wastewater contamination. A comprehensive review of recent research was performed, concentrating on techniques that employ advanced technology for the detection, forecasting, and elimination of marine debris. Research shows that advanced computer vision and machine learning techniques significantly boost the efficiency and precision of pollution detection, such as recognizing plastic waste through satellite images, and improving clean-up strategies by directing collection vessels for maximum effectiveness. Efforts are underway to form partnerships among government entities, industry players, and academic scholars to advance these data-centric solutions. However, obstacles persist; AI systems typically demand significant amounts of data and are subject to time limitations, and it is essential to consider the environmental impacts of AI deployment, including energy use and electronic waste. This paper brings together current applications, assesses their effectiveness and limitations, and highlights gaps in the existing research. The ability of AI to transform ocean waste management is substantial; however, achieving its complete potential necessitates collaboration across disciplines, strict data governance, and thoughtful attention to sustainability in AI research.

Keywords: Artificial intelligence; marine pollution; ocean waste management; machine learning; marine debris detection; plastic pollution; remote sensing; pollution forecasting; clean-up optimization; environmental monitoring; sustainability