Sadaiappan Balamurugan, Balakrishnan Preethiya, C R Vishal, Vijayan Neethu T, Subramanian Mahendran, Gauns Mangesh U
Department of Biology, United Arab Emirates University, Al Ain 971, UAE.
Plankton Laboratory, Biological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa 403004, India.
ACS Omega. 2023 Apr 27;8(18):15831-15853. doi: 10.1021/acsomega.2c06441. eCollection 2023 May 9.
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.
机器学习（ML）是指基于大量数据预测有意义的输出或对复杂系统进行分类的计算机算法。机器学习应用于包括自然科学、工程、太空探索甚至游戏开发在内的各个领域。本综述聚焦于机器学习在化学生物海洋学领域的应用。在全球固定氮水平、二氧化碳分压及其他化学性质的预测中，机器学习的应用是一种很有前景的工具。机器学习还用于生物海洋学领域，从各种图像（如显微镜图像、流动成像仪图像和视频记录）、光谱仪及其他信号处理技术中检测浮游生物形态。此外，机器学习利用声学成功对哺乳动物进行了分类，在特定环境中检测濒危哺乳动物和鱼类物种。最重要的是，利用环境数据，机器学习被证明是预测缺氧状况和有害藻华事件的有效方法，这是环境监测方面的一项重要测量。此外，机器学习被用于构建多个针对不同物种的数据库，这将对其他研究人员有用，新算法的创建将有助于海洋研究界更好地理解海洋的化学和生物学特性。
