Beyond pH Levels: A Comprehensive Survey on Ocean Acidification

This survey explores the transformative role of machine learning in addressing the pervasive threat of ocean acidification on marine ecosystems. Focusing on its predictive capabilities, the paper examines machine learning's efficacy in forecasting pH trends, pinpointing vulnerable species, and assessing broader ecosystem impacts. By training on historical data, machine learning models offer valuable insights for identifying at-risk regions, prioritizing conservation efforts, and optimizing fisheries management. The survey accentuates the integral role of machine learning in unraveling the complexities of ocean acidification while acknowledging challenges like data quality and model robustness. Beyond its theoretical implications, the paper underscores the pragmatic application of machine learning in formulating effective mitigation and adaptation strategies. In doing so, it not only advances understanding of ocean acidification but also sets a roadmap for future research, emphasizing the need for refined data, robust models, and seamless integration of research outcomes into practical solutions for marine ecosystem preservation. Keywords: machine learning, ocean acidification, pH trends, marine ecosystems, vulnerability assessment, conservation strategies, adaptation planning.