Research Symposium

BIO

Injun Cho is an undergraduate student studying environmental science as a major at Florida State University. Currently in his second year, Injun is pursuing a Bachelor of Science degree and has extensive experience in STEM-related research. With the assistance of Dr. Ming Ye, his research mentor, Injun is investigating the credibility of a machine learning model aimed to predict harmful algal blooms in the Port Charlotte region. Injun's goal is to pursue a career in environmental research or environmental law and policy to help preserve ecosystems and give them time to adapt.

The Analysis and Prediction of Harmful Algal Blooms (K.brevis) through Machine Learning Modules

Abstract

Harmful algal blooms (HABs) occur frequently along Florida’s Gulf Coast, affecting marine ecosystems and public health. A machine learning model previously developed for the Port Charlotte region by Elshall at Florida Gulf Coast University is evaluated in this study to determine whether it can generalize across nearby environments. The model is applied to the Sarasota region, specifically the Manatee River basin, using environmental data obtained from NOAA, the Water Atlas, and the National Data Buoy Center. The dataset spans 1994–2023, with data starting from 2023, when 2023 observations were used for model validation. Given the geographic proximity between Port Charlotte and the Peace River Basin, similar predictive performance is anticipated. Model accuracy is assessed by comparing predicted bloom events with observed red tide occurrences. Future work may involve integrating atmospheric dispersion modeling to better estimate inland transport of aerosolized brevetoxins and associated public health impacts.

Keywords: Machine learning, Red Tide, Florida, Environmental