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### Bayesian analysis of hydrological time series based on MCMC algorithm

DOI: 10.23977/acss.2016.11001 | Downloads: 105 | Views: 6947

#### Author(s)

Liu Jinshan 1, Zhao Huiqin 2

#### Affiliation(s)

1 College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
2 Hua Shang College, GuangDong University of Finance & Ecnomics, Guangzhou, China

Liu Jinshan

#### ABSTRACT

In this paper we consider Bayesian analysis of the possible changes in hydrological time series by Markov chain Monte Carlo (MCMC) algorithm. We consider multiple change-points and various possible situations. The approach of Bayesian stochastic search selection is used for detecting and estimating the number and positions of possible change-point in a piecewise constant model. MCMC algorithm is used to estimate the posterior distributions of parameters. The result of the analysis is applied to the hydrological data sets of the major river net area of Shunde in China and the data set of Nile River. In order to further investigate the trends in each segment of the hydrological data sets, we consider the analysis of change-point regression model via MCMC algorithm.

#### KEYWORDS

Bayesian stochastic search, MCMC algorithm, Hydrological time series, Change-point regression model.

#### CITE THIS PAPER

Huiqin, Z. and Jinshan L. (2016) Bayesian analysis of hydrological time series based on MCMC algorithm. Advances in Computer, Signals and Systems (2016) 1: 1-7.

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