Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/11641

TitleClustering and forecasting of dissolved oxygen concentration on a river basin
Author(s)Gonçalves, A. Manuela
Costa, Marco
KeywordsHydrological basin
Water quality
Clustering
State space model
Linear model
Kalman filter
Issue dateFeb-2011
PublisherSpringer Verlag
JournalStochastic Environmental Research & Risk Assessment (serra)
Citation"Stochastic Environmental Research & Risk Assessment." ISSN 1436-3240. 25:2 (Fev. 2011) 151-163.
Abstract(s)The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts.
TypeArticle
URIhttp://hdl.handle.net/1822/11641
DOI10.1007/s00477-010-0429-5
ISSN1436-3240
1436-3259
Publisher versionhttp://www.springerlink.com/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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