A study on the growth characteristics of green algae blooms in the Yellow Sea using categorical analysis

Green algae (Enteromorpha prolifera) bloom, also known as the green tide, is becoming a new type of marine ecological disaster. More and more studies have been conducted to investigate the cause of green tide as well as its growth and drifting characteristics. However, most existing works either rely on single factor analysis or support vector machine-based multifactor analysis that do not have an appropriate mechanism to handle categorical data. In this paper, categorical data analysis is adopted to study the effect of multiple environmental factors to the green algae evolution. Among the five factors we studied, temperature, wind force, and wave height have numerical values but weather condition and wind direction are nominal variables. We focus on the study of the correlation between these environmental factors and the size of distribution area of the green tide. Logistic regression models are built using remote sensing data from the Southern Yellow Sea acquired in 2013. Comprehensive analysis and evaluation are carried out, showing the main factors that affect the size of the bloom to be temperature and wave height, as well as their interaction. We also obtain the influence level of each main factor such that accurate prediction and early warnings of green tide can be made possible to alleviate the damage of the bloom.