In this research,we examine how the Al Hoceima Marine Protected Area(MPA),located in the southwest Mediterranean Sea,can be effectively monitored using the SeaExplorer glider—an advanced autonomous underwater vehicle...In this research,we examine how the Al Hoceima Marine Protected Area(MPA),located in the southwest Mediterranean Sea,can be effectively monitored using the SeaExplorer glider—an advanced autonomous underwater vehicle(AUV)designed for long‑duration oceanographic missions.The study focuses on the glider’s ability to simultaneously observe a variety of environmental parameters,including temperature,conductivity,oxygen,and chlorophyll,during its deployment across multiple transects.The primary objective of the mission is to improve understanding of the vertical thermal structure and seasonal dynamics of the water column in this ecologically signiicant region.To achieve this,we apply Gaussian Process(GP)regression techniques to the glider‑derived temperature data.This statistical method enables the smoothing and interpolation of irregularly spaced in situ measurements,thereby improving the visibility and interpretation of stratiication patterns throughout the water column.Although the glider followed a predetermined course,the data‑driven analysis suggests that adaptive sampling strategies—such as adjustments based on real‑time outliers—could be valuable in future missions.Our results,which show distinct thermal layering and seasonal variability,are crucial for informing ecosystem function assessments and climate resilience planning.This study also discusses how integrating machine learning into glider‑based monitoring could enhance MPA observation systems and promote adaptive,evidence‑based management.展开更多
文摘In this research,we examine how the Al Hoceima Marine Protected Area(MPA),located in the southwest Mediterranean Sea,can be effectively monitored using the SeaExplorer glider—an advanced autonomous underwater vehicle(AUV)designed for long‑duration oceanographic missions.The study focuses on the glider’s ability to simultaneously observe a variety of environmental parameters,including temperature,conductivity,oxygen,and chlorophyll,during its deployment across multiple transects.The primary objective of the mission is to improve understanding of the vertical thermal structure and seasonal dynamics of the water column in this ecologically signiicant region.To achieve this,we apply Gaussian Process(GP)regression techniques to the glider‑derived temperature data.This statistical method enables the smoothing and interpolation of irregularly spaced in situ measurements,thereby improving the visibility and interpretation of stratiication patterns throughout the water column.Although the glider followed a predetermined course,the data‑driven analysis suggests that adaptive sampling strategies—such as adjustments based on real‑time outliers—could be valuable in future missions.Our results,which show distinct thermal layering and seasonal variability,are crucial for informing ecosystem function assessments and climate resilience planning.This study also discusses how integrating machine learning into glider‑based monitoring could enhance MPA observation systems and promote adaptive,evidence‑based management.
