Air quality prediction method based on ensemble extreme learning machine

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) ...

AI Technical Summary

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 extremum in deep learning, and provides an air quality prediction method based on integrated extreme learning machine, which has high prediction accuracy

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