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.

Paroli, R., Spezia, L., Stutter, M., Vinten, A., Bayesian Analysis of a Water Quality Turbidity High Frequency Time Series Through Markov Switching Autoregressive Models, Contributed paper, in CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS, (Firenze, 09-11 September 2021), Firenze University Press, Firenze 2021: 400-403. 10.36253/978-88-5518-340-6 [http://hdl.handle.net/10807/184823]

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

Paroli, R.
Primo
;
2021

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.
Inglese
CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS
13th Scientific Meeting of the Classification and Data Analysis Group
Firenze
Contributed paper
9-set-2021
11-set-2021
978-88-5518-340-6
Firenze University Press
Paroli, R., Spezia, L., Stutter, M., Vinten, A., Bayesian Analysis of a Water Quality Turbidity High Frequency Time Series Through Markov Switching Autoregressive Models, Contributed paper, in CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS, (Firenze, 09-11 September 2021), Firenze University Press, Firenze 2021: 400-403. 10.36253/978-88-5518-340-6 [http://hdl.handle.net/10807/184823]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/184823
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