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AI-Assisted Quality Control of CTD Data (view source)
Revision as of 12:10, 22 December 2022
, 12:10, 22 December 2022no edit summary
==Experimental Model Performance==
==Experimental Model Performance==
[[File:Performance.png|alt=|right|477x477px]]
In order to validate the performance of the model, we have performed experimentation using historical CTD data covering the years 2013-2021. We use a 5-fold cross-validation methodology to ensure robustness in the measured results.
In order to validate the performance of the model, we have performed experimentation using historical CTD data covering the years 2013-2021. We use a 5-fold cross-validation methodology to ensure robustness in the measured results.
Obtaining perfect accuracy will likely never be obtainable as there is uncertainty in the decision-making even for oceanographers. There are many complex factors at play influencing the data that is ultimately recorded such as choppy water causing the scanning equipment to descend irregularly, winds and strong weather conditions causing mixing of waters near the surface, and currents causing mixing of waters under the surface. As the distinction between proper and corrupted scan data can be very difficult to identify under these conditions, the human decision-making process is not guaranteed to be perfect. This in fact highlights a potential area where the development of more mature AI models that can exploit additional information on these factors may have the potential to lead to automated approaches that could eventually augment the human decisions to reach higher overall accuracy.
Obtaining perfect accuracy will likely never be obtainable as there is uncertainty in the decision-making even for oceanographers. There are many complex factors at play influencing the data that is ultimately recorded such as choppy water causing the scanning equipment to descend irregularly, winds and strong weather conditions causing mixing of waters near the surface, and currents causing mixing of waters under the surface. As the distinction between proper and corrupted scan data can be very difficult to identify under these conditions, the human decision-making process is not guaranteed to be perfect. This in fact highlights a potential area where the development of more mature AI models that can exploit additional information on these factors may have the potential to lead to automated approaches that could eventually augment the human decisions to reach higher overall accuracy.
