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Principal-component analysis-based construction method of multivariate hydrological time series matching model

A technology of hydrological time series and principal component analysis, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as poor efficiency and accuracy, and lack of pertinence, so as to improve efficiency, improve efficiency and The effect of improving the accuracy and matching accuracy

Inactive Publication Date: 2018-04-20
HOHAI UNIV
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Problems solved by technology

[0004] Purpose of the invention: Aiming at the shortcomings of the existing multivariate hydrological time series similarity matching methods, which have poor efficiency and accuracy and are not targeted, the present invention provides a method for constructing a multivariate hydrological time series matching model based on principal component analysis

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  • Principal-component analysis-based construction method of multivariate hydrological time series matching model
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  • Principal-component analysis-based construction method of multivariate hydrological time series matching model

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Embodiment Construction

[0025] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0026] Such as figure 1 As shown, the multivariate hydrological time series matching model construction method based on principal component analysis includes the following steps:

[0027] Step 1: In this example, the daily average water level data of four stations in the X River Basin: San XX, Hong XX, Hua XX, and Ge X are used as data sets to extract relatively complete data from 2010 to 2016. It is standardized, and the Z-score standardization method is used here: X i =(X i –μ i ) / σ i , whe...

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Abstract

The invention discloses a principal-component analysis-based construction method of a multivariate hydrological time series matching model. Combined-model construction of multivariate hydrological time series similarity matching is carried out on the basis of principal-component analysis (PCA) and dynamic-time-warping (DTW) methods. The construction method includes: firstly, carrying out isomorphic processing on original data, wherein a Z-score standardization method is adopted; then carrying out piecewise aggregate approximation (PAA) processing on the processed data, and carrying out PCA processing on the data after PAA processing, wherein dimension reduction of the data in both a time dimension and a variable dimension is realized after the two times of processing; and finally, using aweighted DTW method for similarity matching, and obtaining a time series, which is most similar to a given time series, by matching. The construction method improves accuracy and time efficiency of similarity matching, provides services for hydrological forecasting and hydrological data analysis, and has higher application values for needs of water conservancy informatization and water conservancymodernization.

Description

technical field [0001] The invention relates to a method for building a model based on data mining and similarity matching of hydrological time series, in particular, a method for similarity matching of multivariate hydrological time series, matching a given model from historical hydrological time series The sequence with which the time series are most similar. Background technique [0002] With the development of information technology, the types and quantities of hydrological data have increased dramatically, gradually showing the characteristics of diversity, mass and polymorphism. According to the characteristics of multivariate hydrological time series, efficient data mining algorithms are selected to extract useful information and knowledge from a large number of hydrological time series data, and provide new analysis methods and scientific decision support for solving outstanding problems in the field of hydrology. [0003] Traditional hydrological data mining method...

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Application Information

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IPC IPC(8): G06F17/50G06F17/30
CPCG06F16/2465G06F30/20
Inventor 娄渊胜盖振叶枫孙建树
Owner HOHAI UNIV
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