Surface ship safety predictive analysis
Musquiz, Alejandro D.
Roach, Mark A.
Irvine, Nelson J.
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This research seeks to find root causes of Class A or B mishaps in Navy surface ships in order to identify ships at risk for future mishaps. Additionally, by looking at data from ships that experienced mishaps between 2012 and 2017, and by searching beyond the root cause of specific causal factors for these incidents, we may be able to determine if indicator variables could have predicted the ships were at risk. We explored the LHD, LPD (San Antonio Class), and CG ship classes, as these classes experienced the most mishaps between 2012 and 2017. We used linear regression, descriptive statistics, time-series analysis, and data optimization as the primary methods to examine our collected data. We implemented a reverse-forecasting, or backcasting, approach to correlate variables to LHD, LPD, and CG class ships that experienced a Class A or B mishap in the studied years. We were unable to identify a correlation in the numerous data sets. Small amounts of correlation were found in the data models, but nothing statistically significant that would help predict future mishaps.
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