Towards Chl-a bloom understanding by EM-based unsupervised event detection

Marine water quality monitoring and subsequent management require to know when a specific event like harmful algae bloom may occur and which environmental conditions and pressures lead to this event. So, event detection and its dynamic understanding are crucial to adapt strategy. An algorithm is proposed to identify curves mixture and their dynamics features - initiation, duration, peaks and ends of the event. The approach is fully unsupervised, it requires no tuning parameters and is based on Expectation Maximization process to estimate the most robust mixture according to fixed criteria. A complete framework is proposed to deal with a univariate time series with missing data. The approach is applied on Chlorophyll-a series collected weekly since 1989. Chlorophyll-a is a proxy of the phytoplankton biomass. The results are promising according to the phytoplankton composition knowledge, collected at lower frequency, and allowing to discuss about the annual variability of phytoplankton dynamics.