Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/20998

TitlePredicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering
Author(s)Gonçalves, A. Manuela
Costa, Marco
KeywordsHydrological basin
Water quality
State-space modelling
Kalman filter
Distribution-free estimation
Issue date2013
PublisherSpringer
JournalStochastic Environmental Research and Risk Assessment (SERRA)
Abstract(s)This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.
TypeArticle
URIhttp://hdl.handle.net/1822/20998
DOI10.1007/s00477-012-0640-7
ISSN1436-3240
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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