The use of water quality indicators is of crucial importance to identify risks to the environment, society and human health. In particular, the Chlorophyll type A (Chl-a) is a shared indicator of trophic status and for monitoring activities it may be useful to discover local dangerous behaviours (for example, the anoxic events). In this paper we consider a comprehensive data set, covering the whole Adriatic Sea, derived from Ocean Colour satellite data, during the period 2002-2012, with the aim of identifying homogeneous areas. Such zonation is becoming extremely relevant for the implementation of European policies, such the Marine Strategy Framework Directive. As an alternative to clustering based on an "average" value over the whole period, we propose a new clustering procedure for the time series. The procedure shares some similarities with the functional data clustering and combines nonparametric quantile regression with an agglomerative clustering algorithm. This approach permits to take into account some features of the time series as nonstationarity in the marginal distribution and the presence of missing data. A small simulation study is also presented for illustrating the relative merits of the procedure.

CLUSTERING CHLOROPHYLL-A SATELLITE DATA USING QUANTILES

GAETAN, Carlo;Girardi, Paolo;PASTRES, Roberto;
2016

Abstract

The use of water quality indicators is of crucial importance to identify risks to the environment, society and human health. In particular, the Chlorophyll type A (Chl-a) is a shared indicator of trophic status and for monitoring activities it may be useful to discover local dangerous behaviours (for example, the anoxic events). In this paper we consider a comprehensive data set, covering the whole Adriatic Sea, derived from Ocean Colour satellite data, during the period 2002-2012, with the aim of identifying homogeneous areas. Such zonation is becoming extremely relevant for the implementation of European policies, such the Marine Strategy Framework Directive. As an alternative to clustering based on an "average" value over the whole period, we propose a new clustering procedure for the time series. The procedure shares some similarities with the functional data clustering and combines nonparametric quantile regression with an agglomerative clustering algorithm. This approach permits to take into account some features of the time series as nonstationarity in the marginal distribution and the presence of missing data. A small simulation study is also presented for illustrating the relative merits of the procedure.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3666910
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