Abstract
This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications and the evolving landscape of technology in SAR missions, the paper underscores the potential of AI to revolutionize SAR protocols and enhance the USCG's capabilities. It concludes by advocating for the prioritization of research and implementation of predictive analytics AI in non-combat military scenarios, emphasizing its potential to save time, resources, and ultimately, lives.
Faculty Advisor/Mentor
Dr. Saltuk Karahan, Mr. Huong Quach, CDR (ret) USCG Joshua Nelson
Document Type
Paper
Disciplines
Computer and Systems Architecture | Computer Engineering | Data Storage Systems | Navigation, Guidance, Control and Dynamics | Other Computer Engineering
DOI
10.25776/z67m-qt72
Publication Date
4-14-2024
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Included in
Computer and Systems Architecture Commons, Data Storage Systems Commons, Navigation, Guidance, Control and Dynamics Commons, Other Computer Engineering Commons
Predictive AI Applications for SAR Cases in the US Coast Guard
This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications and the evolving landscape of technology in SAR missions, the paper underscores the potential of AI to revolutionize SAR protocols and enhance the USCG's capabilities. It concludes by advocating for the prioritization of research and implementation of predictive analytics AI in non-combat military scenarios, emphasizing its potential to save time, resources, and ultimately, lives.