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

A technology of concentration prediction and neural network, applied in measuring devices, suspension and porous material analysis, scientific instruments, etc., can solve the problems of unable to predict pollutant concentration, not considering, and unable to make accurate predictions, etc.

CN109142171BActive Publication Date: 2021-08-03SHANGHAI NORMAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2021-08-03

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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.
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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 ...

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Embodiment Construction

[0060] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0061] First define the air pollutant concentration prediction:

[0062] Definition 1 Prediction of air pollutant concentration: mainly through historical pollutants and meteorological information, to predict the concentration of a series of air pollution such as PM2.5 and PM10 in a certain period of time in the future. It is one of the key research topics, so it has certain interdisciplinary nature.

[0063] Definition 2 Traditional forecasting methods: non-deep learning air pollutant concentration forecasting methods are collectively referred to as traditional forecasting methods, ...