A PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion

A multivariate statistical analysis and concentration prediction technology, applied in prediction, neural learning methods, calculations, etc., can solve problems such as long convergence time and over-fitting, and achieve the effect of improving accuracy, increasing speed, and eliminating data lag problems

Inactive Publication Date: 2019-06-18
标旗(武汉)环境科技有限公司
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Problems solved by technology

[0003] The purpose of the present invention solves the problem of too long convergence time and overfitting in the tradi...

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  • A PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion
  • A PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion
  • A PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion

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[0037] The invention will be described in detail below in conjunction with specific embodiments.

[0038] In order to further illustrate the present invention, the specific method, implementation process, algorithm analysis, result analysis, etc. of the present invention will be described in detail in conjunction with the above-mentioned drawings.

[0039] Such as figure 1 , figure 2 As shown, the theoretical basis of the present invention includes environmental science, computer science, atmospheric science, statistics and so on. Compared with the traditional prediction method based on the aerodynamic method, the invention involves a wider range of disciplines, and the integration of the disciplines is closer. Therefore, it has a certain interdisciplinary nature and has certain theoretical support for the development of new disciplines and the expansion of forecasting methods. Commonly used analysis and prediction methods include artificial neural networks, genetic algori...

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Abstract

The invention relates to the field of artificial intelligence and big data, in particular to a PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion, which comprises the following steps: analyzing the advantages and disadvantages of the two on the theoretical level, and constructing a fusion algorithm based on the two on the basis; acquiring meteorological data and pollutant data from a provincial control point and a national control point; Sampling data in recent half a year, and analyzing the correlation between each factor and the PM2.5 concentration by using a Pearson correlation coefficient; Dividing all the data into three parts, namely training data, test data and prediction data, training a model by using the training data, and setting related model parameters; Inputting test data into the model; According to the method, by analyzing the time and space characteristics of PM2.5, the data is subjected to dimensionality reduction, the deep data characteristics of PM2.5 are mined through the deep learning technology, the data operation speed is greatly increased, prediction work can be carried out in real time by combining with improvement of precision, and the problem of data lagging is solved.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and big data, in particular to a PM2.5 concentration prediction method based on multivariate statistical analysis and LSTM fusion. Background technique [0002] With the improvement of people's living standards, the ratio of urbanization, industrialization, and population urbanization, that is, the ratio of the three modernizations, continues to increase. However, the over-exploitation of resources and over-consumption of energy have broken the original ecological balance system, and environmental issues have been paid more and more attention by governments of all countries. Especially in recent years, smog weather has frequently appeared, and the problem of PM2.5 pollution is extremely serious. In order to reasonably predict the concentration of PM2.5, experts from various countries have proposed various methods. The prediction methods can be divided into two categories according to the ...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
Inventor 方强何粤城王学锐
Owner 标旗(武汉)环境科技有限公司
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