Monitoring system of phytoplankton blooms by using unsupervised classifier and time modeling

The paper deals with a monitoring system combining K-means classifier and one Hidden Markov Model in order to detect phytoplankton blooms and to understand their dynamics. The states of the Hidden Markov Model and codebook symbols are computed without a priori knowledge thanks to K-means algorithms. The system is tested on database signals from the Marel-Carnot station that registers water characteristics at high frequency resolution. The experiments show that, when the states are set to two, these correspond to phytoplankton productive and non-productive periods. Moreover, when states are set to five, these correspond to the dynamics of phytoplankton blooms.