Intelligent integrated data processing model for oceanic warning system

摘要

There are a number of dirty data in observation data set derived from ocean observing network. These data should be carefully and reasonably processed before they are used for forecasting or analysis in oceanic warning system (OWS). Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS. Firstly, we design an integrated framework of the oceanic data processing and present its processing model. The function of each module of this model is analyzed in details. Then, we propose several intelligent data processing methods, such as an intelligent data cleaning method based on the fuzzy c-means algorithm, a data filtering and clustering method based on the greedy clustering algorithm, and a data processing method based on the maximum entropy for the OWS. The efficiency and accuracy of the proposed model is proved by experimental results of observation data of the Red Tide. The proposed model can automatically find the new clustering center with the updated sample data, and outperforms several algorithms in data processing for the OWS.