Applications of Machine Learning in Chemical and Biological Oceanography

TL;DR

This review highlights how machine learning techniques are increasingly used in chemical and biological oceanography for predicting chemical properties, detecting marine organisms, classifying species, and monitoring environmental conditions.

Contribution

It provides a comprehensive overview of recent applications of machine learning in oceanography, emphasizing new algorithms and their impact on marine research and environmental monitoring.

Findings

ML effectively predicts chemical properties like nitrogen levels and CO2 pressure.

ML accurately detects and classifies marine organisms from various data sources.

ML-based environmental predictions aid in monitoring hypoxia and algal blooms.

Abstract

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.