Title: How to separate long-term trends from periodic variation in water quality monitoring
Abstract: Modelling and multivariate analyses processed on multiple time series usually encounter some difficulties for three reasons: (1) sampling dates may be not equally spaced; (2) several values may be missing; and (3) the usual multivariate analyses may not succeed in separating long-term trends from regular periodic variations on an annual scale within the time series. To circumvent these difficulties, we propose a statistical approach based on the modelling of data by the non-parametric smoother Loess and the application of functional principal components analysis (FPCA). FPCA thereby facilitates the typology of variables based on their long-term trends and/or their periodic variation. We applied this approach to a long-term study over nine years (1983–1991) of the water quality of the Seine river (France) conducted downstream of a plant for wastewater treatment.
Publication Year: 1997
Publication Date: 1997-11-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 20
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