Machine Learning Based Dynamic Correlation on Marine Environmental Data Using Cross-Recurrence Strategy

• Published in: IEEE access Vol. 7; pp. 185121 - 185130
• Main Authors: Li, Zhigang, Cai, Di, Wang, Jialin, Li, Yingqi, Gui, Guan, Sun, Xiaochuan, Wang, Ning, Zhang, Jiabo, Liu, Huixin, Wang, Gang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Correlation; CRP; CRQA; Data visualization; Delays; Machine learning; Marine time series; Natural disasters; phase space reconstruction; Predictive models; Red tides; Similarity; Statistical analysis; Tides
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2960764

As a frequent natural disaster, red tide has attracted more and more attentions. In fact, red tide results from the joint actions of multiple complex marine environmental factors. Unfortunately, there is no work on the interaction analysis between these factors. To inaugurate a systematic research of this area, a novel machine learning based framework is developed for marine environmental series analysis. It combines cross recurrence plot (CRP), cross recurrence quantification analysis (CRQA) and statistical analysis. This framework provides a general way to transform two marine series into a high-dimensional space. CRP is used to visualize internal dynamics, while the influence of factors is quantitatively analyzed through CRQA. Finally, the representative factors in each field are statistically determined by boxplot. This is the first analysis framework attempting to reveal the similarity in intrinsic dynamics of marine factors. Experimental results show that the framework is competent to perform the visualization of marine time series. Besides, the results also demonstrate the degree of similarity between different marine factors through quantitative analysis.