| As we move towards shipboard-underway and automated systems for monitoring water quality and assessing ecological status, there is a need to evaluate how effective the existing monitoring systems are, and how we could improve them. Considering the existing limitations for processing numerous and complex data series generated from automated systems, and because of processes involved in phytoplankton blooms, this paper proposes a data-driven evaluation of an unsupervised classifier to optimize the way we track phytoplankton, including harmful algal blooms (HABs), and to identify the main associated hydrological conditions. We used in situ data from a portable flow-through automatic measuring system coupled with a multi-fixed-wavelength fluorometer... |
