Air quality prediction method based on ensemble extreme learning machine

An extreme learning machine and air quality technology, applied in the field of air quality prediction based on the integrated extreme learning machine, can solve the problem that the multi-layer perceptron is easy to fall into the local extreme value, so as to improve the generalization ability, improve the prediction accuracy and generalization. Ability-enhancing effect

Active Publication Date: 2017-11-07
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] The present invention aims to solve the problem that the multi-layer perceptron in the fully connected layer is easy to fall into local extre

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  • Air quality prediction method based on ensemble extreme learning machine
  • Air quality prediction method based on ensemble extreme learning machine
  • Air quality prediction method based on ensemble extreme learning machine

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[0049] Taking air quality prediction as an example, the following is a detailed description of the present invention with examples and drawings.

[0050] The invention uses a PC and requires a GPU with sufficient computing power to accelerate training. Such as figure 1 As shown, the specific steps of an air quality prediction method based on an extreme learning machine provided by the present invention are as follows:

[0051] Step 1. Construct the input data and output data of the model

[0052] The input vector X = {x 1 , X 2 ,...X i ,...X n } And the output vector Y = {y 1 , Y 2 ,...Y i ,...Y n }. Each variable in X represents a factor related to air quality, such as wind, wind direction, and sulfur dioxide concentration. X takes the historical data of air quality related factors at the current moment, and can also add the forecast value of the weather forecast. Y is the expected output, where each variable represents the air quality for every hour in the next 24 hours at the c...

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Abstract

The invention discloses an air quality prediction method based on an extreme learning machine. The air quality prediction method comprises the steps that step 1, air quality data requiring prediction analysis are acquired and divided into a training data asset and a test data set; step 2, CNN is trained based on the training data set and a model performing in each validation set is selected to act as a feature extraction model; step 3, the activation value of the first layer of the CNN full connection layers acts as the input of GBELM, the GBELM is trained and the GBELM performing the best in each validation set is selected to act as a prediction model; and the GBELM is utilized to replace the CNN full connection layers obtained in the step 2 so as to obtain a final air quality prediction model; and step 4, the test data are inputted to the air quality prediction model to calculate the abstract features of the test data and then inputted to the GBELM to obtain the output value of each ELM to perform summation so as to obtain the prediction result. With application of the technical scheme, the air quality prediction method has high prediction accuracy.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to an air quality prediction method based on an integrated 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 predicts the concentration of pollutants in the air by physically simulating the 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. In 2016, Ruiyun Yu et al., L Wang et al. used Random Forest (RandomForest, RF) ...

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IPC IPC(8): G06N3/04G06N3/08G01N33/00
CPCG06N3/084G01N33/0004G06N3/045
Inventor 刘博闫硕
Owner BEIJING UNIV OF TECH
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