Completion of a Sparse GLIDER Database Using Multi-iterative Self-Organizing Maps (ITCOMP SOM)

Abstract We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCOMP SOM provided reconstructed temperature and salinity profiles that are consistent with the physics of the phenomenon they sampled. A cross-validation test was performed and validated the approach, providing a root mean square error of providing a root mean square error of 0,042 °C for the reconstruction of the temperature profiles and 0,008 PSU for the simultaneous reconstruction of the salinity profiles.