Prediction of PM2.5 concentration based on BP neural network optimized by bee colony algorithm

Jie Heng,Min Li

doi:10.1051/matecconf/202235503025

Abstract

According to the ambient air pollutants data and meteorological conditions data of Mianyang City in 2017, the BP neural network model based on MATLAB is established to predict the daily average PM2.5 concentration of Mianyang City in the next two days. However, the traditional BP network has the disadvantages of slow convergence speed and easy to fall into local optimum. In order to improve the prediction accuracy of the model, an optimization algorithm is added to the prediction model to avoid the model falling into local minimum. In this paper, the bee colony algorithm is added to the prediction model to improve the accuracy of BP neural network prediction model. The data from January to November are used for training, and the data from December are used as the verification results. The results show that the optimization model can accurately predict the daily average PM2.5 concentration of Mianyang City in the next two days, which provides a new idea for the prediction of PM2.5 concentration of the city, provides a theoretical basis for the early warning and decision-making of air pollution, and also provides more reliable prediction services for people’s daily travel.

Highlights

In 2016,253 out of 338 cities in China still had substandard air quality, accounting for 74.8 percent of the total, of which 80.2 percent were days with PM2.5 as the primary pollutant, triggering the red alert
Using Back Propagation Neural Network (BP) artificial neural network as PM2.5 concentration prediction model, its network topology structure is shown in figure 1 from the diagram, we can see that the basic structure of the network structure is divided into three layers, they are the input layer, the hide layer and the output layer[3]
Artificial bee colony algorithm is a kind of artificial intelligence model which simulates the self-organization and self-adaptation behavior of biological community in nature without control, its main advantage is that it searches in the process of each global iteration, so that the probability of finding the optimal solution is greatly increased, the optimized algorithm was added to the BP neural network model to predict the average daily concentration of PM2.5 over the two days in Mianyang

Summary

Introduction

In 2016,253 out of 338 cities in China still had substandard air quality, accounting for 74.8 percent of the total, of which 80.2 percent were days with PM2.5 as the primary pollutant, triggering the red alert. The data of meteorological conditions are obtained from Mianyang Meteorological Bureau, including the daily average of the maximum temperature, the minimum temperature, the average temperature, the pressure, the maximum wind speed, the maximum wind speed, the average wind speed and the relative humidity. The data taken are for the whole year from 1 January 2017 to 30 December 2017. Data from January to November of the same year were selected for training, and data from December of the same year were used as the verification results. We add the bee colony algorithm into the prediction model, and make use of the advantages of the parallel global search of the optimization algorithm to improve the stability, learning and efficiency

BP neural network prediction model

The limitation of BP neural network

Principles of swarm algorithm

Prediction data of traditional BP neural network

Findings

Conclusion