Document details for 'Periodic multivariate Normal hidden Markov models for the analysis of water quality time series'

Authors Spezia, L., Futter, M.N. and Brewer, M.J.
Publication details Environmetrics 22, 304-317.
Abstract The modelling of multivariate riverine water quality time series poses some challenging problems including: weak dependency between observations; nonlinearity; non-Normality; seasonality and missing data. We demonstrate that periodic multivariate Normal hidden Markov models (MNHMMs) are appropriate tools to analyse riverine water quality time series. We introduce a fully Bayesian inference procedure for this class of models, where the number of hidden states of the Markov process is unknown and reversible jump Markov chain Monte Carlo (RJMCMC) methods are developed.We present a case study using long-term dissolved inorganic nitrogen time series measured in three Scottish rivers. Our results show the strength of the hidden Markov multistate approach for analysing long-term multivariate riverine water quality time series.
Last updated 2012-04-20

Unless explicitly stated otherwise, all material is copyright © Biomathematics and Statistics Scotland.

Biomathematics and Statistics Scotland (BioSS) is formally part of The James Hutton Institute (JHI), a registered Scottish charity No. SC041796 and a company limited by guarantee No. SC374831. Registered Office: JHI, Invergowrie, Dundee, DD2 5DA, Scotland