PM2. 5 Prediction Based on Genetic Algorithm and Regularized Extreme Learning Machine

Abstract

Environmental quality is closely related to people’s health and has always been a research hotspot. In this paper, the daily average values of PM2. 5 are predicted by atmospheric data such as NO2 and PM10 in Changsha City in 2017, and the BIC criterion is used for feature selection. On the basis of the traditional over-limit learning machine (ELM), the regularization term is introduced to control the complexity of the model, and the input layer weight matrix and the hidden layer threshold matrix of the model are optimized by genetic algorithm (GA) to establish the genetic algorithm. Then the PM2. 5 prediction model of the regularized limit learning machine (GA-RE-ELM) is built, the experiment shows that the model achieves more state of the art performance than the BP neural network and the over-limit learning machine, the mean square error is reduced by 35.09% and 25.49%, the average absolute error is reduced by 40.86% and 30.80%, and the average absolute percentage error is reduced by 45.49% and 31.65%. Meanwhile, it provides a new method for predicting PM2. 5 concentration.

Publication
Computer Science and Application

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