A knowledge-guided and manual intervention-based gene expression programming for PM2.5 concentration prediction

Abstract

In view of the lack of interpretation and inability to know the occurrence mechanism of PM2.5 concentration by deep learning algorithm in solving PM2.5 concentration prediction problem, this paper adopts a knowledge-guided and manual intervention-based gene expression programming (KMGEP) to solve it. The KMGEP algorithm not only has strong model learning ability, but also can obtain the explicit function relationship between PM2.5 concentration and its influencing factors. In the process of algorithm implementation, knowledge guidance and manual intervention are introduced to GEP for predicting PM2.5 concentration so as to improve its global optimization ability and convergence speed. In this paper, the daily PM2.5 concentration prediction in winter (from December to February) in Xi’an region is taken as an example, and the KMGEP algorithm is compared with the artificial neural network back propagation algorithm (BP-ANN) and the convolutional neural network and long short-term memory neural network combined model (CNN-LSTM). Experimental results show that the KMGEP algorithm not only has high prediction accuracy in solving the PM2.5 concentration prediction, but also the obtained function expression can reveal the occurrence relationship between PM2.5 concentration and its influencing factors.