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PM2.5 concentration prediction method based on feature vectors and least square support vector machine

A support vector machine and eigenvector technology, applied in special data processing applications, instruments, electrical digital data processing, etc.

Inactive Publication Date: 2014-08-27
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention provides a method for predicting PM2.5 concentration based on eigenvectors and least squares support vector machines to solve the problems of PM2.5 concentration prediction and PM2.5 concentration prediction accuracy

Method used

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  • PM2.5 concentration prediction method based on feature vectors and least square support vector machine
  • PM2.5 concentration prediction method based on feature vectors and least square support vector machine
  • PM2.5 concentration prediction method based on feature vectors and least square support vector machine

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Embodiment 1

[0050] Embodiment 1: as Figure 1-4 As shown, a PM2.5 concentration prediction method based on eigenvectors and least squares support vector machine, first collects the pollutant concentration data related to PM2.5 concentration for preprocessing; then calculates the comprehensive meteorological index; .5 Concentration-related pollutant concentration data and comprehensive meteorological index are carried out for correlation analysis, and the eigenvectors containing the comprehensive meteorological index are obtained to form the eigenvector A and the eigenvectors obtained by removing the comprehensive meteorological index are used to form the eigenvector B; finally, through the eigenvector A, The feature vector B constitutes the training sample to train the LS-SVM model and evaluate the prediction results.

[0051] The concrete steps of described method are as follows:

[0052] Step1. Collect the pollutant concentration data related to PM2.5 concentration for preprocessing: s...

Embodiment 2

[0067] Embodiment 2: as Figure 1-4 As shown, a PM2.5 concentration prediction method based on eigenvectors and least squares support vector machine, first collects the pollutant concentration data related to PM2.5 concentration for preprocessing; then calculates the comprehensive meteorological index; .5 Concentration-related pollutant concentration data and comprehensive meteorological index are carried out for correlation analysis, and the eigenvectors containing the comprehensive meteorological index are obtained to form the eigenvector A and the eigenvectors obtained by removing the comprehensive meteorological index are used to form the eigenvector B; finally, through the eigenvector A, The feature vector B constitutes the training sample to train the LS-SVM model and evaluate the prediction results.

[0068] The concrete steps of described method are as follows:

[0069] Step1. Collect the pollutant concentration data related to PM2.5 concentration for preprocessing: s...

Embodiment 3

[0084] Embodiment 3: as Figure 1-4 As shown, a PM2.5 concentration prediction method based on eigenvectors and least squares support vector machine, first collects the pollutant concentration data related to PM2.5 concentration for preprocessing; then calculates the comprehensive meteorological index; .5 Concentration-related pollutant concentration data and comprehensive meteorological index are carried out for correlation analysis, and the eigenvectors containing the comprehensive meteorological index are obtained to form the eigenvector A and the eigenvectors obtained by removing the comprehensive meteorological index are used to form the eigenvector B; finally, through the eigenvector A, The feature vector B constitutes the training sample to train the LS-SVM model and evaluate the prediction results.

[0085] The concrete steps of described method are as follows:

[0086] Step 1. Collect the pollutant concentration data related to PM2.5 concentration for preprocessing: ...

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Abstract

The invention relates to a PM2.5 concentration prediction method based on feature vectors and a least square support vector machine, and belongs to the field of environment pollution prediction. The method includes the steps that firstly, pollutant concentration data relevant to the PM2.5 concentration are collected and preprocessed; then comprehensive weather indexes are calculated; correlation analysis is conducted on the pollutant concentration data relevant to the PM2.5 concentration and the comprehensive weather indexes, and then feature vectors containing the comprehensive weather indexes are acquired to form feature vectors A and feature vectors in which the comprehensive weather indexes are removed are acquired to form feature vectors B; eventually, a training sample training LS-SVM model is formed through the feature vectors A and the feature vectors B, and the prediction result is evaluated. By the combination of environment monitoring data and the actual situation, air humidity, wind power and humidity are combined with a formation mechanism of PM2.5, and the new concept of a comprehensive weather index formula is provided; prediction accuracy is high.

Description

technical field [0001] The invention relates to a PM2.5 concentration prediction method based on eigenvectors and least squares support vector machines, belonging to the field of environmental pollution prediction. Background technique [0002] PM2.5 refers to particulate matter with an aerodynamic equivalent diameter less than or equal to 2.5 μm (microns) in the ambient air. The higher the value, the higher the concentration of particulate matter, and the more serious the air pollution. Although PM2.5 is only a small part of the earth's atmospheric composition, it has an important impact on indicators such as air quality and visibility. Recently, smog has appeared in many places in my country, which has seriously affected people's lives. Sulfur dioxide, nitrogen oxides and PM2.5 are the main constituents of smog. PM2.5 is the culprit that aggravates smog pollution and has become an important indicator affecting people's normal life. Accurately predict the concentration of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 贺建峰李龙马磊邵党国易三莉相艳刘立芳
Owner KUNMING UNIV OF SCI & TECH
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