Discriminating important ocean salinity and temperature patterns in argo data

Abstract

Ocean salinity and temperature variations have been observed for decades to clarify their effect to global climate changes. Data mining techniques are effective in extracting implicit and useful information from large databases. Discovering salinity and temperature variation patterns from Argo ocean data will in turn help reveal the spatio-temporal relationship between salinity and temperature variations. However, some of the discovered patterns are trivial because they are already known to the oceanographer. In this study, the water mass (a water body with the same salinity and temperature), the mined salinity and temperature patterns and an entropy importance measure are combined to discriminate important patterns from trivial patterns. This study measures both the patterns with variations in both antecedent and consequent parts that belong to separate clusters, and that belong to the same cluster. A pattern is classified as important if its importance measure exceeds a predefined threshold. The important patterns are transformed into fuzzy rules in a fuzzy inference model to obtain more accurate salinity and temperature variation predictions. Simulation results verify the effectiveness of the proposed model.