Urban PM10 Concentration Prediction Method Based on Feature Expansion Fusion Neural Network

Abstract

The present invention relates to a kind of urban PM10 concentration prediction method based on the fusion neural network of feature expansion, comprising: step S1: based on the stacked self-encoder and LSTM network of feature expansion, build the model of urban PM10 concentration prediction; Step S2: from pollution Select the training data and test data from the monitoring data of objects and weather; Step S3: Use the training data to train the stacked autoencoder based on feature expansion; Step S4: Based on the Gaussian function to the output feature vector of the stacked autoencoder process, calculate the corresponding influence weights for the feature vectors of different cities, and obtain new feature vectors by weighted summation; Step S5: Input the new feature vectors into LSTM for overall training of the model; Step S6: Input the test data The trained model is used to measure the error of the prediction result generated by the test data; step S7: the trained and fine-tuned model is used to predict the concentration of air pollutants. Compared with the prior art, the present invention has the advantages of accurate prediction and the like.

Description

technical field [0001] The invention relates to a method for predicting PM10 concentration, in particular to a method for predicting urban PM10 concentration based on a fusion neural network of feature expansion. Background technique [0002] Air pollution is a problem that has been widely concerned in daily life, and with the increasingly serious problem of air pollution, the types of air pollutants are becoming more and more diverse, the formation and diffusion of air pollutants are becoming more and more complicated, and the prediction of pollutant concentration is no longer necessary. It is single-point, but dynamic and regionally linked. Therefore, in the current situation, in order to make more accurate predictions of pollutant concentration, prevent the occurrence of heavy pollution incidents, and improve the level of environmental management and decision-making, we should make full use of the monitored pollutants and meteorological big data, and fully mine and learn ...

AI Technical Summary

Problems solved by technology

These methods have played great advantages in the past when the environmental situation was simple and the data scale was small. However, in the case of massive pollutant data and meteorological data, these methods lack in-depth analysis of data characteristics and cannot fully learn At the same time, these methods regard the change of pollutant concentration as a discrete event, and do not consider and cannot perform correlation analysis in time and space, so that accurate pollutant concentration prediction cannot be performed

[0004] On the other hand, considering that it is difficult to obtain a complete data set for the prediction of air pollutant concentration, most of them have missing features and insufficient feature dimensions, which makes the prediction model unable to fully learn data features and mine the relationship between data, so that it cannot make accurate predictions

At present, there are very few technical researches on expanding the feature dimension in the prediction model in the academic circle, but this technique is very effective in dealing with the problem of insufficient feature dimension

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