Hybrid Hidden Markov Model for Marine Environment Monitoring

Abstract

Phytoplankton is an important indicator of water quality assessment. To understand phytoplankton dynamics, many fixed buoys and ferry boxes were implemented, resulting in the generation of substantial data signals. Collected data are used as inputs of an effective monitoring system. The system, based on unsupervised hidden Markov model (HMM), is designed not only to detect phytoplancton blooms but also to understand their dynamics. HMM parameters are usually estimated by an iterative expectation-maximization (EM) approach. We propose to estimate HMM parameters by using spectral clustering algorithm. The monitoring system is assessed based on database signals from MAREL-Carnot station, Boulogne-sur-Mer, France. Experimental results show that the proposed system is efficient to detect environmental states such as phytoplankton productive and nonproductive periods without a priori knowledge. Furthermore, discovered states are consistent with biological interpretation.