DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS

Abstract

This paper presents a detailed exploration of the transformative role of Machine Learning (ML) in oceanographic research, encapsulating the paradigm shift towards more efficient and comprehensive analysis of marine ecosystems. It delves into the multifaceted applications of ML, ranging from predictive modeling of ocean currents to in-depth biodiversity analysis and deciphering the complexities of deep-sea ecosystems through advanced computer vision techniques. The discussion extends to the challenges and opportunities that intertwine with the integration of AI and ML in oceanography, emphasizing the need for robust data collection, interdisciplinary collaboration, and ethical considerations. Through a series of case studies and thematic discussions, this paper underscores the profound potential of ML to revolutionize our understanding and preservation of oceanic ecosystems, setting a new frontier for future research and conservation strategies in the realm of oceanography.