Machine Learning Classification Algorithms for Predicting Karenia brevis Blooms on the West Florida Shelf

Harmful algal blooms (HABs), events that kill fish, impact human health in multiple ways, and contaminate water supplies, have increased in frequency, magnitude, and impacts in numerous marine and freshwaters around the world. Blooms of the toxic dinoflagellate Karenia brevis have resulted in thousands of tons of dead fish, deaths to many other marine organisms, numerous respiratory-related hospitalizations, and tens to hundreds of millions of dollars in economic damage along the West Florida coast in recent years. Four types of machine learning algorithms, Support Vector Machine (SVM), Relevance Vector Machine (RVM), Naive Bayes classifier (NB), and Artificial Neural Network (ANN), were developed and compared in their ability to predict these blooms. Comparing the 21 year monitoring dataset of K. brevis abundance, RVM and NB were found to have better skills in bloom prediction than the other two approaches. The importance of upwelling-favorable northerly winds in increasing K. brevis probability, and of onshore westerly winds in preventing blooms from dispersing offshore, were quantified using RVM, and all models were used to explore the importance of large river flows and the nutrients they supply in regulating blooms. These models provide new tools for management of these devastating algal blooms.

Automated summary

The frequency, magnitude, and impact of harmful algal blooms (HABs) have increased globally. Machine learning algorithms, such as RVM and NB, show better abilities in predicting blooms. The importance of upwelling-favorable winds, onshore winds, and river flows in regulating blooms has been quantified.