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Air quality prediction method based on psode-bp neural network

A PSODE-BP, BP neural network technology, applied in the field of air quality prediction, can solve the problems of low efficiency and complicated implementation process, and achieve the effect of reducing the number, increasing the convergence accuracy, and improving the air prediction accuracy.

Active Publication Date: 2021-11-26
ZHEJIANG GONGSHANG UNIVERSITY
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AI Technical Summary

Problems solved by technology

The air pollutant numerical prediction method is a scientific and effective method through mathematical modeling of the change law of air pollutants and the use of mathematical models to approximate the change trend of pollutants, but the implementation process is relatively complicated and the efficiency is not high

Method used

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  • Air quality prediction method based on psode-bp neural network
  • Air quality prediction method based on psode-bp neural network
  • Air quality prediction method based on psode-bp neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2015

[0046] Example From January 2015 to February 2016 PM in a certain place 10 Daily mean concentration forecasts.

[0047] Prepare training sample data: PM of a local environmental automatic monitoring station from January 1, 2015 to December 30, 2016 10 Concentration data and air pollution monitoring and weather forecast data.

[0048] 1), PSODE-BP neural network prediction model construction, the process is as follows:

[0049] (1.1) Determination of the number of nodes in the input layer and output layer

[0050] Select the factors with larger comprehensive influence weights as the neurons in the input layer of the respective neural networks. The relevant meteorological factors and air pollution factors in this example are daily average data, which are used as the neurons in the input layer of the PSODE-BP network prediction model, and determine the input layer nodes The number is 10 and the number of output layer nodes is 1.

[0051] (1.2) Determination of the number of h...

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Abstract

The invention discloses an air quality prediction method based on PSODE-BP neural network. The present invention collects data, then analyzes the data effectively, determines the number of input nodes, the number of output nodes and the number of hidden layer nodes of the BP neural network according to the characteristics of the data; then uses the CPSODE algorithm to optimize the connection weight of the BP neural network value and threshold, and get the final BP neural network prediction model; after the BP neural network training, the convergence accuracy of the adaptive PSO‑DE‑BP network is increased; the invalid iteration of the PSO‑DE network can be reduced; Optimizing the preferred particle for the PSO reduces the fitness. Reduced the number of iterations of the BP neural network.

Description

technical field [0001] The invention relates to an air quality prediction method, in particular to an air quality prediction method based on PSODE-BP neural network. Background technique [0002] The problem of air pollution is not a problem of a certain city, a certain region or a certain country, but a major problem faced by all mankind. Atmospheric pollution or even worsening will cause serious global problems. The first step in air control is to take monitoring measures to keep abreast of the discharge of pollutants, so that effective measures can be taken in a targeted manner to prevent problems before they happen. [0003] Early monitoring of air pollutants mainly relied on manual sampling and experimental analysis. The vigorous development of computer technology and communication technology has brought technological innovation to the field of environmental monitoring, and the field of environmental monitoring has moved towards informatization and intelligence. Worl...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/02G01W1/10
CPCG01W1/10G06N3/02G06Q10/04
Inventor 王效灵张伟宋艳玲
Owner ZHEJIANG GONGSHANG UNIVERSITY
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