Method for predicting atmospheric pollution conditions based on integrated gate recurrent unit neural network GRU

Abstract

The invention discloses a method for predicting the atmospheric pollution condition based on an integrated gate recursion unit neural network GRU, and the method comprises the steps: 1), carrying outthe multi-modal feature extraction of an atmospheric pollutant data set S11 (t) through a local mean decomposition function LMD, and obtaining an atmospheric pollutant feature data set; 2) establishing a gate recursion unit neural network GRU by using the training data set, and training the gate recursion unit neural network GRU by using the training data set; 3) inputting the normalized differenttypes of feature data sets into a gate recursion unit neural network GRU, and outputting a normalized sub-mode prediction value; and 4) performing multi-modal feature estimation value integration onthe normalized sub-mode prediction value by adopting inverse LMD operation to obtain a trained LMD-GRU neural network model. According to the method, the problems that the performance of the model islower than that of a multi-mode feature learning model, the precision is low and the actual prediction effect is not ideal due to the fact that feature learning is not obvious when the LSTM model performs regression prediction on haze are solved.

Description

technical field [0001] The invention relates to the technical field of environmental engineering detection, in particular to a method for predicting air pollution based on an integrated gate recursive unit neural network GRU. Background technique [0002] Environmental problems are becoming more and more serious, and air pollution has serious adverse effects on the environment, human health, and social economy, especially particulate matter with an aerodynamic equivalent diameter of less than 2.5 mm (PM2.5), which is more likely to be inhaled, leading to high mortality, chronic diseases Exacerbated, respiratory and cardiac diseases worsened. To protect public health by providing early warnings, PM2.5 concentration prediction is an important and effective work. On the basis of accurate and reliable modeling, the concentration of pollutants in the next few hours can be released to the public to guide early warning decision-making activities. [0003] Current research on air p...

AI Technical Summary

Problems solved by technology

However, when the model processes weather data for regression prediction, it still starts from a single model, and the internal characteristics of sequence data are not clearly expressed.

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