temporal-spatial association analysis of ocean salinity and temperature variations
Ocean circulation plays an important role in global climate change. In an effort to monitor ocean circulation an infrastructure of more than 3,000 buoys have been deployed in the open water to measure ocean salinity and temperature variations. Some of these data are made freely available by Argo. The focus of this study is extracting previously unknown patterns of abnormal ocean salinity and temperature variations from Argo data that can be further applied to predict ocean current variations. First, Argo data are converted to market-basket type data that are used to find temporal-spatial association rules. The discovered rules reveal the associations of abnormal ocean salinity and temperature variations. Next, the discovered temporal and spatial variation patterns are used to predict future ocean salinity and temperature variations surrounding Taiwan. A 3-D visualization model is developed to present a) the interactions between events at different dates, concentric circles and ocean depths, and b) relationships between ocean temperature and salinity variations. The proposed 3-D visualization model help researchers determine whether the ocean temperature and salinity variations occurred in the same water mass and the relative importance of each attribute. Having established the informative relationships among different attributes an early warning system for climate changes can be established such that their impact on property and loss of life is reduced. The discovered association rules are compared with traditional association rules to illustrate their strength in analyzing global climate change.
climate changes temporal-spatial association rules data mining