Clustering and forecasting of dissolved oxygen concentration on a river basin
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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.
KeywordsHydrological basin Water quality Clustering State space model Linear model Kalman filter
The authors would like to thank the anonymous referees for many helpful critics and suggestions that contributed to improve this paper. The authors would like to thank to Eng. Pimenta Machado from the Portuguese Regional Directory for the Northern Environment and Natural Resources and to Eng. Cláudia Brandão from the Portuguese Institute of Water, for sharing their skills and experiences and for supplying the monitored data. A. Manuela Gonçalves acknowledges the financial support provided by the Research Centre of Mathematics of the University of Minho through the FCT Pluriannual Funding Program.
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