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An air quality prediction method based on variational autoencoder and extreme learning machine

An extreme learning machine and air quality technology, applied in neural learning methods, forecasting, instruments, etc., can solve problems such as poor filling accuracy and poor prediction accuracy, achieve generalization ability improvement, improve prediction accuracy, improve generalization performance and The effect of forecast accuracy

Active Publication Date: 2021-08-13
BEIJING UNIV OF TECH
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide an air quality prediction method based on variational autoencoder and extreme learning machine, to solve the problem of poor prediction accuracy caused by missing value filling accuracy in air quality prediction, and to use deep learning technology Further improve prediction accuracy

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  • An air quality prediction method based on variational autoencoder and extreme learning machine
  • An air quality prediction method based on variational autoencoder and extreme learning machine
  • An air quality prediction method based on variational autoencoder and extreme learning machine

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Embodiment Construction

[0015] Taking air quality prediction as an example, the following is a detailed description of the present invention in combination with examples and accompanying drawings.

[0016] The present invention uses a PC and requires a GPU with sufficient computing power to accelerate training. Such as Figure 1 As shown, the concrete steps of a kind of air quality prediction method based on variational autoencoder and extreme learning machine provided by the present invention are as follows:

[0017] Step 1. Acquire air quality data and encode the data using VAE

[0018] 1) Use any method to obtain air quality data, generally including weather data and pollutant data.

[0019] 2) Construct the input X of the VAE with non-missing data vae ={x 1 ,x 2 ,...x i ,...x n}, since VAE is an autoencoder, the output vector is also X. Each variable in X represents an input vector, and the elements of the vector are factors related to air quality, such as wind force, wind direction, sulf...

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Abstract

The invention discloses an air quality prediction method based on a variational autoencoder and an extreme learning machine, comprising the following steps: step 1, acquiring air quality data and encoding the data using VAE; step 2, dividing the encoded data into Training data and test data; step 3, train the RNN to process the encoded air quality, and input the output of the RNN into a fully connected neural network; step 4, input the output of the trained RNN into the ELM, and train ELM; step 5, input the test data into the RNN, and then input all the output results of the RNN into the ELM to obtain the final output results. The technical scheme of the invention is adopted to solve the problem of poor prediction accuracy caused by poor filling accuracy of missing values ​​in air quality prediction.

Description

technical field [0001] The invention belongs to the technical field of data mining, in particular to an air quality prediction method based on a variational autoencoder and an extreme learning machine. Background technique [0002] At present, the main means of air quality prediction is the numerical simulation method, among which CMAQ (CommunityMultiscale Air Quality) is the most popular method. The numerical simulation method realizes the prediction of air quality through physical simulation of air quality related factors. The numerical simulation method can reflect the influence mechanism of air quality-related factors on air quality because of the physical simulation, but the simulation requires a lot of calculations, so the speed is very slow. In today's era of big data, machine learning has become a very important prediction method and has successfully solved problems in many fields. Yang Siqi et al. used Random Forest (RF) and Support Vector Machine (SVM) to predict...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06N3/084G06Q10/04G06Q50/26G06N3/045
Inventor 刘博闫硕
Owner BEIJING UNIV OF TECH
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