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A PM2.5 Concentration Prediction Method

A concentration prediction and optimization algorithm technology, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of difficult training of neural network weights and thresholds, low accuracy of PM2. Excellent problems to achieve the effect of improving accuracy

Inactive Publication Date: 2021-03-02
POTEVIO INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] However, when the neural network model is used to predict the PM2.5 concentration, the weight and threshold of the neural network are not easy to train, and the network is easy to fall into a local optimum, resulting in a low accuracy rate of the neural network model for PM2.5 concentration prediction

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  • A PM2.5 Concentration Prediction Method
  • A PM2.5 Concentration Prediction Method
  • A PM2.5 Concentration Prediction Method

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Embodiment Construction

[0019] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0020] At present, the existing technologies mostly use neural networks to predict the concentration of PM2.5. BP neural network is one of the most widely used neural network models at present. When predicting PM2.5, the meteorological data (such as weather, temperature, wind speed, wind direction, etc.) and the concentration of pollutants in the air (such as NO x , SO 2 , O 3 etc.) as the input of the neural network, and the PM2.5 concentration as the output of the network.

[0021] The following is a preliminary introduction to the corresponding BP neural network.

[0022] BP (Back Propagation) neural network is a multi-layer feed-forward network trained by the erro...

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Abstract

The invention provides a PM2.5 concentration prediction method. According to the method, a grey wolf optimization algorithm and a BP neural network are combined, a weight value and a threshold value of the BP neural network are optimized by utilizing the grey wolf optimization algorithm, and the PM2.5 concentration is predicted by adoption of an optimized model. The method has the beneficial effect of improving the PM2.5 concentration prediction correctness.

Description

technical field [0001] The present invention relates to the technical field of air quality prediction, and more specifically, to a PM2.5 concentration prediction method. Background technique [0002] PM2.5 refers to particulate matter in the air with a kinetic equivalent diameter of 2.5 microns or less. It has the characteristics of small particle size, large area, strong activity, and easy to attach toxic and harmful substances. Moreover, it stays in the atmosphere for a long time and has a long transportation distance. It can directly enter the lungs of the human body and affect human health. When the concentration of PM2.5 in the air is high, it will form different degrees of smog and reduce air visibility. The main factors affecting the concentration of PM2.5 are meteorological factors (such as weather, temperature, wind speed, wind direction, etc.) and the concentration of pollutants in the air (such as NO x , SO 2 , O 3 etc.) effects. PM2.5 has had a serious impa...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/00G06N3/08
CPCG06N3/006G06N3/084
Inventor 李书霞
Owner POTEVIO INFORMATION TECH CO LTD