Change-point analysis of hydrological time series using grey relational method

Wong, Hie Hua; Hu, Bao Qing; Ip, W. H.; Xia, Jun

doi:10.1016/j.jhydrol.2005.10.007

Cited by 83 publications

(38 citation statements)

References 42 publications

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“…(2) MCMC method detects one change-point at 1899 for the Nile river series, which agrees with previous studied by [6]. (4) The change time of Ban Sha Wei,Gan Zhu, Nan Sha, San Shui and Wan Qing Sha are more earlier than those detected by [6] using a grey relational method.…”

Section: Data Analysis With Hydrological Datasupporting

confidence: 86%

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Bayesian analysis of hydrological time series based on MCMC algorithm

2016

ACSS

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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.

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Section: Data Analysis With Hydrological Datasupporting

confidence: 86%

“…(4) The change time of Ban Sha Wei,Gan Zhu, Nan Sha, San Shui and Wan Qing Sha are more earlier than those detected by [6] using a grey relational method. …”

Section: Data Analysis With Hydrological Datamentioning

confidence: 99%

Bayesian analysis of hydrological time series based on MCMC algorithm

2016

ACSS

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“…Grey relational analysis (GRA) is usually used to analyze the mutual relation of different variables, according to the similarities of geometric proximity between different discrete sequences [46]. In practice, the geometric proximity is described by the grey relational degree, which can be arranged in sequential order [47]. An advantage is that the sample and distribution of data are unrestricted in the application of GRA.…”

Section: Grey Relational Analysismentioning

confidence: 99%

Impacts of Urbanization on River Systems in the Taihu Region, China

Deng

Han

et al. 2015

Water

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River systems are valuable to human beings; meanwhile, they are intensively influenced by human activities, especially urbanization. In this study, based on the data derived from topographic maps and remote sensing images, the temporal and spatial change of river system geomorphology in the Taihu Region over the past 50 years was investigated in conjunction with urbanization. Results demonstrated that the number of river systems decreased drastically, that the morphology of river channels changed into wider and straighter and that the structure of river network tended to simplify in the Taihu Region in recent 50 years. Meanwhile, the changes in river density, the water surface ratio, the river development coefficient, the main river area length ratio and the box dimension in the rapid urbanization period were much greater than those in the slow urbanization period, but the decrease of river sinuosity in the slow urbanization period was more intense. Moreover, the spatial differences of the changes in the river development coefficient were the largest, and the changes in the river indicators in the low-urbanized regions were the most intense. In addition, the changes in the water surface ratio had the closest correlation with urbanization, and the relational degrees between population urbanization and the changes in river systems were the largest. The results can provide a reliable basis to determine reasonable management and conservation strategies of river systems in the Taihu Region. OPEN ACCESSWater 2015, 7 1341

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“…The proximity is described by the grey relational grade, which is a measure of the similarities of discrete data that can be arranged in sequential order. Further information on this concept and examples of its application are available in the literature [23].…”

Section: Basic Ideamentioning

confidence: 99%

“…In the early 1980s, Deng [21] formally introduced the grey system theory to handle uncertainties in small data samples with imprecise information, and this field has expanded to include grey relational analysis (GRA), grey modeling (GM), and prediction theory [20,22]. To date, grey system theory has been applied extensively in many fields, including hydrology, manufacturing, and financial analyses [23][24][25]. For groundwater, Gau et al [26] used GRA to identify factors for the selection of suitable groundwater recharge sites, using hydrogeological data obtained with a groundwater flow model.…”

Section: Introductionmentioning

confidence: 99%

Identifying Major Factors Affecting Groundwater Change in the North China Plain with Grey Relational Analysis

Xue

Zhang

2014

Water

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Abstract:The North China Plain (NCP) is facing a water crisis under the dual impact of natural and anthropogenic factors. Groundwater levels have declined continuously since 1960, causing a series of environmental problems that have restricted sustainable development in the region. In the present study, we first utilized a previously developed 3D groundwater model to determine changes in groundwater level in the region over the past 50 years . We then applied grey relational analysis (GRA) to identify and ordinate major factors that have contributed to these changes. The results show an overall decreasing trend in groundwater levels in this region over the past 50 years and an increase in the water table depth at a rate of approximately 0.36 m/a. Groundwater exploitation showed the most significant correlation with the groundwater table decline, when compared with other factors including precipitation and river leakage. Therefore, human activities should be considered the primary force driving the groundwater level down. The results of this study have implications for developing criteria that consider changes in both climate and socio-economics. Furthermore, since the NCP is one of the most water-scarce OPEN ACCESSWater 2014, 6 1582 and densely populated regions in the world, the analytical approach used in and the insights gained from this study are of international interest.

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Change-point analysis of hydrological time series using grey relational method

Cited by 83 publications

References 42 publications

Bayesian analysis of hydrological time series based on MCMC algorithm

Bayesian analysis of hydrological time series based on MCMC algorithm

Impacts of Urbanization on River Systems in the Taihu Region, China

Identifying Major Factors Affecting Groundwater Change in the North China Plain with Grey Relational Analysis

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