Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorise complex systems based on a large amount of data. ML applied in a variety of areas, including natural science, engineering, space exploration, and even gaming development. This article focused 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 utilised in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM and video recorder), 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 the harmful algal bloom events, an important 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.