SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control.
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We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly i.....
JournalFrontiers in Marine Science
Sustainable Development Goals (SDG)14.A
Essential Ocean Variables (EOV)Sea surface temperature
Maturity LevelTRL 9 Actual system "mission proven" through successful mission operations (ground or space)
Best Practice TypeManual (incl. handbook, guide, cookbook etc)
CitationMieruch, S., Demirel, S., Simoncelli, S., Schlitzer, R. and Seitz, S. (2021) SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control. Frontiers in Marine Science, 8:611742, 10pp. DOI: 10.3389/fmars.2021.611742
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