JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2014, Vol. 44 ›› Issue (6): 19-25.doi: 10.6040/j.issn.1672-3961.1.2014.180

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A prediction method of atmospheric PM2.5 based on DBNs

ZHENG Yi, ZHU Chengzhang   

  1. College of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, China
  • Received:2014-01-23 Revised:2014-10-27 Online:2014-12-20 Published:2014-01-23

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Abstract: A DBNs-based (deep belief networks) method for forecasting the daily average concentrations of PM2.5 in Xian was proposed. Besides, the way to select training data set as well as the DBNs parameters was optimized. Then relative experiments and comparison with methods based on BP (back propagation) and RBF (radial basis function) artificial neural network confirmed the feasibility and precision of DBNs. The results showed that the MSE (mean square error) between DBNs simulated PM2.5 daily average concentrations and observed ones was 8.47×10-4 mg2/m6, while the MSE of RBF and BP was 1.30×10-3 mg2/m6 and 1.96×10-3 mg2/m6 respectively. Therefore the DBNs-based method was fit for prediction of PM2.5 concentrations and it predicted more accurately than those methods based on RBF and BP artificial neural network.

Key words: restricted boltzmann machine, deep belief networks, PM2.5 prediction, deep learning, machine learning

CLC Number: 

  • TU457
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