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A Feature Selection Decomposition Method Applied to River Water Level Prediction Data

A feature selection and data prediction technology, applied in the direction of electrical digital data processing, special data processing applications, complex mathematical operations, etc., can solve problems such as lack of information, prediction deviations, etc., to improve prediction accuracy, improve precision, and predict performance Enhanced effect

Inactive Publication Date: 2019-12-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, due to its extraction effect, this effect will introduce potential information loss during the model construction phase, which will cause bias in prediction
In addition, the wavelet transform coefficient result of DWT is related to the starting position of wavelet transform, which brings a certain degree of contingency

Method used

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  • A Feature Selection Decomposition Method Applied to River Water Level Prediction Data
  • A Feature Selection Decomposition Method Applied to River Water Level Prediction Data
  • A Feature Selection Decomposition Method Applied to River Water Level Prediction Data

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Embodiment Construction

[0046] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the implementations shown and described in the drawings are only exemplary, intended to explain the principle and spirit of the present invention, rather than limit the scope of the present invention.

[0047] Embodiments of the present invention provide a feature selection decomposition method applied to river water level prediction data, such as figure 1 As shown, including the following steps S1-S6:

[0048] S1. Collect the hydrological elements that affect the water level of the target prediction site (including the current water level information of the target site, the water level information of the upstream basin, and the rainfall along the way and other hydrological elements).

[0049] In the embodiment of the present invention, the water level trend in the middle and lower reaches of Chishui River is taken as ...

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Abstract

The invention discloses a feature selection decomposition method applied to river water level prediction data. In order to obtain the most suitable features as model input, the invention introduces LASSO regression to perform feature selection on the original input set, and integrates MODWT to perform feature selection on the selected features. The components were decomposed and the performance of LASSO‑MODWT was tested using multiple linear regression as the underlying model. The test shows that the feature selection decomposition method based on LASSO‑MODWT is beneficial to improve the performance and model interpretation ability of the river water level prediction model.

Description

technical field [0001] The invention belongs to the technical field of water level prediction, and in particular relates to the design of a feature selection decomposition method applied to river water level prediction data. Background technique [0002] Water level prediction plays an extremely important role in flood control and disaster reduction, water resource utilization and distribution management. A robust water level prediction model can provide relevant decision makers with information on future water level changes, and timely grasp potential hydrological disasters, so that relevant early warning deployments can be made earlier. In the field of water level prediction, due to the multi-dimensionality and complexity of factors affecting water level, the potential input quantities of the model system often present nonlinear dynamic relationships and multiple correlations. In addition, the number of input quantities is generally large, especially after the lag of each...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/14G06F17/18G06F17/50
CPCG06F17/148G06F17/18G06F30/20G06F30/333
Inventor 杨拥军管杰
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA