Air micro-station concentration prediction method based on LSTM neural network

A neural network and concentration prediction technology, applied in the field of environmental monitoring based on neural network, can solve problems such as spatial instability and overfitting

Pending Publication Date: 2021-09-10
INTELLIGENT MFG INST OF HFUT
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for predicting air micro-station concentration based on LSTM neural network, so as to solve the problems of spatial instability and over-fitting in the prior art deep learning method for concentration prediction

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  • Air micro-station concentration prediction method based on LSTM neural network
  • Air micro-station concentration prediction method based on LSTM neural network
  • Air micro-station concentration prediction method based on LSTM neural network

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Embodiment Construction

[0026] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] Such as figure 1 As shown, in this embodiment, the deep learning neural network LSTM model (long short-term memory neural network) is used to solve the instability and overfitting in the current air quality prediction process by fusing small batch gradients, discarding neurons and L2 regularization algorithms The problem.

[0028] The specific process of this embodiment is as follows:

[0029] Step 1: Obtain the gas concentration and particle concentration of the air micro-station to construct a data set. A total of 12,000 samples are used in the data set, which are divided into a test set and a training set. The training set and the test set each account for 6,000 samples.

[0030] The isolated forest method is used to remove abnormal data from the acquired data. Specifically, by randomly dividing the features, a random forest is established,...

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Abstract

The invention discloses an air micro-station concentration prediction method based on an LSTM neural network. An isolated forest algorithm is adopted to preprocess pollutant concentration data obtained by an air micro-station, and a batch gradient descent algorithm is fused in deep learning to improve the stability of the whole system. Meanwhile, a Dropout algorithm and an L2 regularization algorithm are added into an input layer and a hidden layer to avoid the over-fitting phenomenon, and the whole algorithm is used for processing the complex space-time relation of particulate matter concentration, gas concentration input and multiple air quality outputs through a low-cost sensor.

Description

technical field [0001] The invention relates to the field of environmental monitoring methods based on neural networks, in particular to an air micro-station concentration prediction method based on LSTM neural networks. Background technique [0002] Air pollution has risen to dangerous levels due to increased transportation, increased population density, increased global warming and abrupt changes in the climate. There is a need to monitor and control pollution to create a healthier and non-toxic environment for human, animal and plant life. Environmental protection agencies and governments have made enormous efforts to reduce the impact of air pollution on communities. Detailed information on air pollution conditions can help researchers, policy makers, and developers manage and improve living conditions, so accurate air quality monitoring is essential. However, due to the complexity of ambient air factors, temperature, humidity, wind speed, etc. will affect the concentr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/26G06N3/04G06N3/00
CPCG06Q10/04G06Q10/06393G06Q10/067G06Q50/26G06N3/006G06N3/044
Inventor 方勇王威胡俊涛
Owner INTELLIGENT MFG INST OF HFUT
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