Feature-based clustering of global sea level anomaly time series.

The efficiency of various sea level change prediction methods can be enhanced through clustering global sea levels, considering the high dimensionality, redundancy, and nonlinearity of sea level anomaly time series. Most clustering algorithms cannot yield satisfactory results when directly applied to the original time series. In this work, the trend and periodic characteristics of global sea level change were analysed by using sea surface high anomaly time series. Then, a feature series considering trend and periodic characteristic constraints was constructed. Finally, the types of global sea level anomaly time series were determined by using the clustering methods. The experimental results reveal the following: (1) Sea level characteristics vary by location. (2) The iterative self-organizing data analysis technique algorithm demonstrates superior clustering performance compared to fuzzy c-means clustering and the method of ordering points to identify the clustering structure. (3) The global sea level anomaly time series can be categorized into nine classes, which are similar to ocean current spatial distributions. The clustering performance of the constructed sea level anomaly feature series surpasses both the original series and the feature series after principal component analysis. This work establishes the trend-predict constrained clustering framework for global sea level anomalies, and the derived clusters serve as foundational elements for our forthcoming automated prediction optimization system.