Medium-and-long-term forecasting method for screening factors based on mutual information and principal component analysis

A technology of principal component analysis and mutual information, which is applied in the field of medium and long-term forecasting based on mutual information and principal component analysis screening factors, can solve the problems of stay, increase of sample observation error, increase of network structure complexity, etc., and achieve good effect and reduce The effect of high number and model stability

Pending Publication Date: 2019-03-19
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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Problems solved by technology

However, it is difficult to avoid too many input variables by using mutual information screening factors, which will increase the sample observation error and increase the complexity of the network structure, which is not conducive to the improvement of the stability o

Method used

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  • Medium-and-long-term forecasting method for screening factors based on mutual information and principal component analysis
  • Medium-and-long-term forecasting method for screening factors based on mutual information and principal component analysis
  • Medium-and-long-term forecasting method for screening factors based on mutual information and principal component analysis

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specific Embodiment

[0078] The measured annual runoff series of Xiying Reservoir from 1970 to 2016 were selected for statistical analysis, and the runoff extreme value ratio Km and variation coefficient Cv were used to describe the interannual variation of runoff at each station. The larger the Km and Cv, the greater the interannual variation of runoff; otherwise, the smaller the interannual variation of runoff. The coefficient of variation of the annual discharge of Xiying Reservoir is calculated to be 0.17, and the interannual extreme value ratio of runoff is 2.05. The values ​​of Cv and Km are both small, indicating that the interannual variation of annual runoff is small, the distribution of runoff in different years is relatively stable, and the overall trend is flat and slightly rising. See the appendix for the change process of runoff figure 1 shown.

[0079] The annual runoff of Xiying Reservoir is forecasted by the above-mentioned method provided by the present invention, which can be ...

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Abstract

The invention discloses a medium and long term forecasting method based on mutual information and principal component analysis screening factors, and relates to the field of hydrological forecasting.The method comprises the following steps of: firstly, analyzing a forecasting factor which greatly affects the predicted runoff based on a mutual information-based factor screening method; extractingmain components of the factors by using a main component analysis method; the optimization of forecasting factors of the forecasting model is realized; according to the method, the linear and nonlinear relationship between two variables is fully considered, the information of the overlapped part is removed, the number of original variables is reduced, and the characteristics of a research object are typically shown, so that the obtained medium and long term runoff forecasting model is better in forecasting effect and higher in model stability compared with a traditional method.

Description

technical field [0001] The invention relates to the field of hydrological forecasting, in particular to a medium and long-term forecasting method based on mutual information and principal component analysis screening factors. Background technique [0002] Scientifically and rationally carrying out medium- and long-term hydrological forecasting is the basis for the management and development of water resources in the basin, and it is of great significance to promote social and economic development. [0003] At present, medium and long-term hydrological forecast methods mainly include time series method, fuzzy mathematics method, artificial neural network method, gray system method, wavelet analysis method, chaos theory method, support vector machine method, optimal combination prediction method, etc. Among them, artificial neural network, as a highly parallel radial basis network, has strong nonlinear mapping ability, is suitable for solving various nonlinear, fuzzy, uncertai...

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Application Information

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IPC IPC(8): G06Q10/04G06F16/2458G06F16/29
CPCG06Q10/04
Inventor 雷晓辉王超王旭丁公博廖卫红杨明祥蒋云钟王佳
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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