Research on PM2.5 Prediction Model Based on Neural Network and Deep Learning
REN Ying
WANG Siyuan
XIA Bisheng
Abstract：The problem of atmospheric pollution is becoming more and more serious,and the hazy weather with PM2.5 as the main factor has seriously affected the life of residents.Accurate and efficient PM2.5 concentration prediction is important for environ-mental pollution management.Neural networks and deep learning,as a popular research technology in the direction of artificial intel-ligence in recent years,have become indispensable tools in the field of environmental engineering because of their powerful data analysis,nonlinear fitting and feature extraction capabilities.This paper introduces five common methods of neural networks and deep learning in PM2.5 prediction,which are BP neural network,recurrent neural network,convolutional neural network,radial basis neural network,and feedback neural network,analyzes the advantages and disadvantages of the five models,describes the prediction of PM2.5,and finally outlooks the future development direction of deep learning in the field of PM2.5 prediction.
Keywords：neural networkdeep learningBP neural networkrecurrent neural networkconvolutional neural network
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 332-337,346 )