PM10 Concentration Forecasting Based on CEEMDAN, SE and LSTM Neural Network

Abstract

To improve the predicting accuracy of PM10 concentration prediction, this paper presents a combined prediction model of PM10 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) and long short-term memory (LSTM). The PM10 concentration time series is decomposed into a series of restructured subsequences with obvious complexity differences by CEEMDAN-SE firstly. Then, by adding meteorological parameters to each different restrict-ured subsequence, the LSTM prediction model is built. By adding the prediction results, the final results are got. Meanwhile, the data of four monitoring stations in Tangshan city is used to implement simulation experiment. Experiment results confirm that the proposed prediction model compares with other prediction models to show high prediction precision, and good universality.