A prediction method for air pollutant index based on information entropy and back propagation neural network

By introducing information entropy and backpropagation neural networks in the prediction of atmospheric pollutants, the problems of complexity of meteorological data processing and low prediction accuracy in the prior art are solved, and more efficient and accurate prediction of pollutant concentrations are achieved.

CN114971006BActive Publication Date: 2025-03-21南京口袋宝网络科技有限公司
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Patent Information

Application Number
CN202210559826.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-05-21
Publication Date
2025-03-21
Estimated Expiration
2042-05-21

AI Technical Summary

Technical Problem

When predicting the concentration of atmospheric pollutants, it is difficult to effectively process complex meteorological data, resulting in high time and space complexity and increasing algorithm complexity.

Method used

The atmospheric pollutant index prediction method based on information entropy and backpropagation neural network is adopted. By calculating the information entropy of each meteorological factor, the meteorological factor with the largest information entropy is selected to correlate it with atmospheric pollutants. The backpropagation neural network training model is used to predict the future atmospheric pollutant concentration value.

Benefits of technology

This method not only reduces time complexity and spatial complexity, but also improves the accuracy of prediction of atmospheric pollutant concentrations.

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Abstract

The present invention belongs to the technical field of atmospheric pollution index analysis, and discloses an atmospheric pollutant index prediction method based on information entropy and back propagation neural network, establishes a correlation between each meteorological data and all atmospheric pollutants, and calculates the similarity between each atmospheric pollutant concentration and all meteorological factors with a time gradient, then regards the time series similarity as the degree to which the meteorological data affects the atmospheric pollutant concentration, calculates the information entropy of each meteorological data, and finally selects the meteorological data with the largest information entropy and the atmospheric pollutant to use the back propagation neural network training model, and predicts the future atmospheric pollutant concentration value. Compared with the prior art, the present invention applies the information entropy in physics to the atmospheric pollutant prediction, greatly reduces the influence of different parameters on the prediction results, reduces the complexity of the prediction algorithm, and improves the prediction accuracy.
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