Air quality prediction method based on convolution full-connection bidirectional gating circulation unit

A cycle unit and air quality technology, applied in the field of deep learning, can solve the problems of extracting deep features, strong timing, and high complexity of meteorological data, and achieve accurate prediction results and good performance

Pending Publication Date: 2020-06-12
NANJING UNIV OF INFORMATION SCI & TECH
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Due to the high complexity and strong timing of meteorological data, it is difficult for a single CNN network or RNN network to adapt to the current fo

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  • Air quality prediction method based on convolution full-connection bidirectional gating circulation unit
  • Air quality prediction method based on convolution full-connection bidirectional gating circulation unit
  • Air quality prediction method based on convolution full-connection bidirectional gating circulation unit

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[0025] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0026] This patent proposes a PM2.5 prediction method based on convolutional fully connected bidirectional gated recurrent units. The specific structure is as follows figure 1 shown. In general, since different time series always have different representations and structures, and air quality-related time series have different statistical properties, shallow machine learning models cannot handle complex scenarios well. Therefore, many researchers have studied hybrid deep learning models, which are often effective in improving the performance of classical deep learning models. In the method of this paper, the model can effectively deal with various complex scenarios and time series. When used on air quality data, the model can effectively predict the air pollution indicators at the next moment, especially the concentration of PM2.5 , so as to achieve the effe...

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Abstract

The invention discloses an air quality prediction method based on a convolutional full-connection bidirectional gating circulation unit, and the method comprises the steps: extracting the depth features contained in meteorological data through a convolutional neural network, learning the time sequence and coherence of the data through a cyclic neural network, and finally predicting the needed dataat the next moment through a full-connection layer. In the patent, because meteorological data has no high requirement for anti-rotation performance, a special pooling-layer-free convolutional network is selected to extract related features. According to the method, the air pollution index at the next moment can be accurately predicted in a large number of learning samples. On the whole, the method not only can predict a single index, but also can predict all indexes of input data characteristics, reasonable prediction can be carried out in combination with global characteristics to a certainextent, and the prediction function in the patent is realized.

Description

technical field [0001] The invention belongs to the field of deep learning, and in particular relates to an air quality prediction method based on a convolution fully connected bidirectional gated cyclic unit. Background technique [0002] With the rapid development of science and technology, artificial intelligence and deep learning have been widely used. At present, the methods of smog prediction can be roughly divided into three categories: the first category is statistical methods, the second category is traditional forecasting methods, and the third category is deep learning algorithms. Statistical methods include linear regression, gray system forecasting, Markov forecasting, etc. Most statistical models have certain requirements on data, and these models also have a relatively clear mathematical form, it is difficult to describe data with complex components with limited mathematical formulas. The traditional method is simple, the theory is mature, and it is easy to ...

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Application Information

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IPC IPC(8): G06N3/04G06Q10/04G06Q50/26G01N15/06G01W1/10
CPCG06Q10/04G06Q50/26G01N15/06G01W1/10G06N3/045
Inventor 王保卫孔维纹朱志宏
Owner NANJING UNIV OF INFORMATION SCI & TECH
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