Artificial Intelligence for Sustainable Ocean Management Using Satellite Data
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1291Keywords:
Artificial Intelligence, Sustainable Ocean ManagementAbstract
The integration of artificial intelligence and satellite remote sensing provides an innovative approach to sustainable ocean management. This study demonstrates how oceanographic sensors and AI‑driven predictive models enhance the monitoring and governance of Marine Protected Areas (MPAs) and sustainable fishing zones. Multivariate datasets are used to map areas of high primary productivity in the Gulf of Mexico, employing QGIS and ArcGIS for spatial analysis. Long Short‑Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks trained on historical time series forecast ecological risks, including hypoxic zones and harmful algal blooms. Unsupervised clustering and dimensionality reduction identify anomalies relative to natural oceanographic patterns, supporting more adaptive and precautionary ocean governance.
The integration of artificial intelligence and satellite remote sensing provides an innovative approach to sustainable ocean management. This study demonstrates how oceanographic sensors and AI‑driven predictive models enhance the monitoring and governance of Marine Protected Areas (MPAs) and sustainable fishing zones. Multivariate datasets are used to map areas of high primary productivity in the Gulf of Mexico, employing QGIS and ArcGIS for spatial analysis. Long Short‑Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks trained on historical time series forecast ecological risks, including hypoxic zones and harmful algal blooms. Unsupervised clustering and dimensionality reduction identify anomalies relative to natural oceanographic patterns, supporting more adaptive and precautionary ocean governance.
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