Bayesian Analysis of a Water Quality Turbidity High Frequency Time Series Through Markov Switching Autoregressive Models

Roberta Paroli, L. SPEZIA, M. STUTTER, A VINTEN

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

In order to provide simulation inputs for investigations on diffuse water pollution and support rural land management policy on soil and water management, a turbidity time series recorded in a Scottish stream for more than a year, along with two covariates, is considered. Turbidity time series have complex dynamics because they are non-linear, non-Normal, non-stationary, with a long memory, and present missing values. Given these issues the turbidity process is analysed by Markov switching autoregressive models under the Bayesian paradigm using novel evolutionary Monte Carlo algorithms. Hence, it is possible to efficiently fit the actual data, reconstruct the sequence of hidden states, restore the missing values, and classify the observations into a few regimes, providing new insight on turbidity dynamics.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS
Pagine400-403
Numero di pagine4
DOI
Stato di pubblicazionePubblicato - 2021
Evento13th Scientific Meeting of the Classification and Data Analysis Group - Firenze
Durata: 9 set 202111 set 2021

Convegno

Convegno13th Scientific Meeting of the Classification and Data Analysis Group
CittàFirenze
Periodo9/9/2111/9/21

Keywords

  • non-homogeneous hidden Markov chain
  • water quality
  • population Markov chain MonteCarlo
  • path sampling

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