Mixed-Effect State Space models applied to environmental data: the discrimination of the Water Quality Monitoring Sites in River Vouga
 
 
Description:  The surface water quality monitoring is an important concern of public organizations due to its relevance for public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is to discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed using distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogenous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results.
Date:  2016-04-15
Start Time:   14:30
Speaker:  Magda Monteiro (ESTGA & CIDMA, Univ. Aveiro)
Institution:  ESTGA & CIDMA, Univ. Aveiro
Place:  Sala 5.5
Research Groups: -Probability and Statistics
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