The Gulf of Mexico in trouble: Big data solutions to climate change science - Sunkara, Vishwamithra; McKenna, Jason; Kar, Soumyashree; Iliev, Iliyan; Bernstein, Diana N. (Frontiers in Marine Science, 2023)
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