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 originale | Inglese |
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Titolo della pubblicazione ospite | CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS |
Pagine | 400-403 |
Numero di pagine | 4 |
DOI | |
Stato di pubblicazione | Pubblicato - 2021 |
Evento | 13th Scientific Meeting of the Classification and Data Analysis Group - Firenze Durata: 9 set 2021 → 11 set 2021 |
Convegno
Convegno | 13th Scientific Meeting of the Classification and Data Analysis Group |
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Città | Firenze |
Periodo | 9/9/21 → 11/9/21 |
Keywords
- non-homogeneous hidden Markov chain
- path sampling
- population Markov chain MonteCarlo
- water quality