Prediction of PM2.5 value by BP artificial neural network in honeybee colony

In this paper, propose a BP artificial neural network optimized by artificial bee colony algorithm with a PM2.5 value for the current Nanning area. In this paper, aiming at the shortcomings of BP neural network prediction, the global optimization performance of artificial bee colony algorithm is use...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2020-08, Vol.558 (4), p.42041
Main Authors: Ai, Jiaoyan, Gong, Wenyang
Format: Article
Language:English
Subjects:
Air quality
Algorithms
Artificial neural networks
Back propagation networks
Chemical composition
Chlorophyll
Global optimization
Local optimization
Neural networks
Optimization algorithms
Particulate matter
Prediction models
Search algorithms
Swarm intelligence
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Summary:In this paper, propose a BP artificial neural network optimized by artificial bee colony algorithm with a PM2.5 value for the current Nanning area. In this paper, aiming at the shortcomings of BP neural network prediction, the global optimization performance of artificial bee colony algorithm is used to provide the optimal global initial value for BP neural network and avoid the neural network falling into local optimization. To make the prediction model more accurate. PM2.5 is a small part of the atmospheric chemical composition, which has a great impact on the air quality and thus on the human body. With people's attention to their own health nowadays, it is of great practical significance to predict the PM2.5 content in a certain period in the future. The results show that the average square error and average relative error of the prediction of PM2.5 content based on BP artificial neural network are only 1.25% and 2.46% respectively. BP artificial neural network based on bee colony can be well applied to the prediction of chlorophyll a content, providing a reference for the prediction of PM2.5 content in a certain period in the future.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/558/4/042041