Automation of species-specific cyanobacteria phycocyanin fluorescence compensation using machine learning classification

High-frequency cyanobacteria monitoring often uses in-situ fluorescence of phycocyanin (f-PC). However, f-PC must be calibrated for the dominant cyanobacteria species, and it cannot distinguish cyanobacteria taxa, which relies on conventional time-consuming cyanobacteria identification methods. This study proposes a framework to automate f-PC species-specific compensation through three components: (1) prediction of the dominant cyanobacteria species using data-driven models and routine environmental monitoring data; (2) determination of species-specific f-PC per biomass in controlled laboratory experiments; and (3) automation of f-PC species compensation. The framework was validated by applying it to Myponga drinking water reservoir in South Australia. Three machine learning techniques using only high-frequency water temperature data were compared to predict the dominant cyanobacteria species. The framework application to Myponga drinking water reservoir improved the agreement of f-PC with conventional cyanobacteria biovolume measurements, and provided rapid, low-cost identification of the dominant cyanobacteria species, which can support proactive species-targeted cyanobacteria management