Precipitation prediction method and system

A forecasting method and forecasting system technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of difficult precipitation and low precision, and achieve the effect of improving forecasting accuracy and reducing forecasting errors

Pending Publication Date: 2020-11-13
HUNAN UNIV
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Problems solved by technology

[0002] Because precipitation is affected by many physical elements such as atmospheric circulation, hydrometeorological elements, and physical geography, it is a weakly correlated, highly complex nonlinear dynamic system, and its inter

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  • Precipitation prediction method and system
  • Precipitation prediction method and system

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specific Embodiment

[0153] The precipitation prediction method proposed by the present invention is used to carry out prediction research on the future precipitation of the Wujiang River Basin from 2019 to 2028. The basic data is the precipitation data collected by 58 meteorological stations from 1961 to 2018. image 3 It is the interannual trend chart of precipitation in the Wujiang River Basin from 1961 to 2018.

[0154] Due to the complexity of precipitation forecasting, especially because high-frequency components are random variables and non-stationary signals, it is difficult to accurately predict using conventional forecasting methods. The present invention proposes a new combined precipitation forecasting model.

[0155] The data is preprocessed. Complete the data storage management, the basic analysis of the relationship between the mean and variance of the data, the calculation of the correlation coefficient between the data, etc. Obvious wrong data is eliminated through analysis, and...

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Abstract

The invention discloses a precipitation prediction method and system. The method comprises the steps of collecting historical precipitation data; processing the historical precipitation data to obtainprocessed precipitation data; decomposing the processed precipitation data by adopting an MEEMD method to obtain a plurality of decomposition items and remainder items with different frequencies; determining a precipitation prediction model; wherein the precipitation prediction model comprises a trained convolutional neural network, an optimized support vector machine and an optimized BP neural network; and predicting the precipitation through the precipitation prediction model according to the decomposition items and the remainder items. According to the method, the MEEMD is utilized to decompose the precipitation data into different decomposition items, the convolutional neural network, the particle swarm optimization support vector machine and the BP neural network optimized by the artificial ant colony algorithm are adopted to establish the combined prediction model for the different decomposition items, prediction errors caused by data non-smoothness are reduced, and the prediction precision is improved.

Description

technical field [0001] The invention relates to the field of precipitation forecasting, in particular to a precipitation forecasting method and system. Background technique [0002] Because precipitation is affected by many physical elements such as atmospheric circulation, hydrometeorological elements, and physical geography, it is a weakly correlated, highly complex nonlinear dynamic system, and its interannual variation does not move in a fixed cycle. Instead, it contains changes and local fluctuations on various time scales. This characteristic makes medium and long-term precipitation forecasting more difficult and less accurate. Contents of the invention [0003] The purpose of the present invention is to provide a precipitation forecasting method and system for fast and accurate forecasting of precipitation. [0004] To achieve the above object, the present invention provides the following scheme: [0005] A precipitation forecasting method, comprising: [0006] C...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/00G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/006G06N3/08G06N3/045
Inventor 王永涛刘坚李蓉索鑫宇陈琳
Owner HUNAN UNIV
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