Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 444-449.doi: 10.11896/JsJkx.190700158

• Database & Big Data & Data Science • Previous Articles     Next Articles

PM2.5 Concentration Prediction Method Based on CEEMD-Pearson and Deep LSTM Hybrid Model

DING Zi-ang, LE Cao-wei, WU Ling-ling and FU Ming-lei   

  1. College of Sciences,ZheJiang University of Technology,Hangzhou 310023,China
  • Published:2020-07-07
  • About author:DING Zi-ang, born in 1995, postgradua-te.His main research interests include data processing and deep learning.
    FU Ming-lei, born in 1981, Ph.D, asso-ciate professor.His main research in-terests include Signal processing, deep learning and intelligent robot.
  • Supported by:
    This work was supported by the Special ProJect of “One Belt and One Road” of ZheJiang Science and Technology Department (2015C04005).

Abstract: PM2.5 is well-known as the key indicator for measuring the concentration of air pollutants.It is of great significance for both academic study and applications to make accurate prediction of future PM2.5concentration values by excavating the time series characteristics of PM2.5historical data.However,the correlation of time series data of the original PM2.5concentration value has great influence on the prediction accuracy of the model.In order to solve this problem,a PM2.5concentration prediction me-thod based on CEEMD-Pearson and deep LSTM hybrid model was proposed in this paper.The CEEMD modal decomposition me-thod is adopted to decompose the PM2.5concentration historical data at different frequencies,and to enhance the timing characte-ristics of the data.Then,the Pearson correlation test method is used to screen the different frequency IMFs after decomposition,and the filtered enhancement data is input to the input layer of the deep LSTM network of multiple hidden layers for training and prediction.Experimental data shows that the prediction accuracy of the CEEMD-LSTM hybrid mo-del is 80%.However,the model converges after 7000 training times.While by means of the secondary screening of Pearson correlation test,the model converges after 800 training times,and the prediction accuracy is improved to 87%.At last,the hybrid model combines CEEMD-Pearson with deep LSTM neural network has the best training effect.It converges after 650 training times,and the prediction accuracy reaches 90%.Experimental results show that the CEEMD modal decomposition method can show the hidden time series characteristics in historical data.The secondary screening combined with Pearson correlation analysis can effectively improve the convergence speed and prediction accuracy of model training.Therefore,based on the CEEMD-Pearson and deep LSTM hybrid models,the best training result,the fastest convergence speed and the most accurate prediction result can be obtained,which can effectively solve the PM2.5concentration prediction problem.

Key words: CEEMD, Deep neural network, Hybrid model, LSTM, Pearson, PM2.5

CLC Number: 

  • TP183
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