AUTHOR=Sunkara Vishwamithra , McKenna Jason , Kar Soumyashree , Iliev Iliyan , Bernstein Diana N. 

TITLE=The Gulf of Mexico in trouble: Big data solutions to climate change science

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1075822

DOI=10.3389/fmars.2023.1075822

ISSN=2296-7745

ABSTRACT=The latest technological advancements in the sensor development and production have
4 increased the usage of remote ocean sensors expanding data volume and rates. The extensive
5 data collection efforts to monitor and maintain the health of marine environment with the increase
6 in data volumes supports the data driven learning which can help policy makers in taking effective
7 decisions. Machine learning techniques show a lot of promise for improving the quality and scope
8 of marine research by detecting implicit patterns and hidden trends, especially in huge datasets
9 that are difficult to analyze with traditional methods. Machine learning poses a great potential
10 when integrated with marine science and is extensively used on data collected elsewhere but
11 has not been applied in a significant way to Gulf of Mexico (GOM) generated data. The GOM
12 is the 9th largest water body on Earth with unique features in geology, hydrology, climate and
13 economic aspects. We review several issues marine environments in GOM are facing in addition
14 to climate change and its effects. We also present machine learning techniques and methods
15 used elsewhere to address similar problems and propose applications to problems in the GOM.
16 We anticipate this manuscript will act as a baseline for data science researchers and marine
17 scientists to solve problems in the GOM collaboratively and/or independently