Developing data-driven models for quantifying Cochlodinium polykrikoides using the Geostationary Ocean Color Imager (GOCI)

Abstract

Harmful algal blooms have caused critical problems worldwide because they pose serious threats to human health and aquatic ecosystems. In particular, red tide blooms of Cochlodinium polykrikoides have caused serious damage to aquaculture in Korean coastal waters. In this study, multiple linear regression, regression tree (RT), and Random Forest models were applied to detect C. polykrikoides blooms in coastal waters. Five types of input data sets were implemented to test the performance of the models. The observed number of C. polykrikoides cells and reflectance data from Geostationary Ocean Color Imager images obtained in a 3-year period (2013-2015) were used to train and validate the models. The RT model demonstrated the best prediction performance when four bands and three-band ratio data were simultaneously used as input data. The results obtained via iterative model development with randomly chosen input data indicate that the recognition of patterns in the training data caused variations in the prediction performance. This work provides useful tools for reliable estimation of the number of C. polykrikoides cells using reasonable coastal water reflectance data sets. It is expected that administrators and decision-makers whose work is associated with coastal waters will be able to easily access and manipulate the RT model.