基于外界影响及时序因素的PM2.5预测研究

doi:10.16180/j.cnki.issn1007-7820.2022.03.008

Abstract

Abstract:

As the haze problem gradually worsens, the prediction of one of its main component PM_2.5 has become a widespread concern. The daily concentration of PM_2.5 is affected by many factors, and it has the characteristics of non-linear and time-varying, which is difficult to accurately predict.To solve this problem, a prediction method of PM_2.5 daily concentration based on external influences and time-series factors is proposed. With this method, the main external factors and time factors of PM_2.5 daily concentration are separated, and the BP neural network preliminary prediction model based on the main external factors and the combined residual correction model of EEMD-LSTM neural network based on time factor are established. The daily PM_2.5 concentration and other related factors data of Hangzhou from 2014 to 2019 are used for simulation experiments. The results show that compared with other models, the root mean square error of the prediction model proposed in the study is 2.74, and the prediction accuracy is higher.

Key words: haze, PM_2.5, BP, EEMD, LSTM, time series prediction, neural network, time series decompose, combination prediction

CLC Number:

TP39

Yanmei YANG,Zongmao CHENG. Prediction of PM_2.5 Based on External Influences and Time-Series Factors[J].Electronic Science and Technology, 2022, 35(3): 51-57.

Figures/Tables 10

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参考指标	比较指标	相关性系数
PM₂_.₅	PM₁₀	0.946 0
	SO₂	0.638 8
	CO	0.758 9
	NO₂	0.695 5
	O₃	-0.106 0
	平均温度	-0.406 9
	最高温度	-0.347 3
	最低温度	-0.444 4
	平均相对湿度	-0.155 9
	总降雨量/总融雪量	-0.199 7
	平均风速	-0.297 0
	最大持续风速	-0.274 6

输入数据	输出数据
x₁,x₂,…,x_n	x_n+1
x₂,x₃,…,x_n+1	x_n+2
?	?
x_N-n,x_N-n+1,…,x_N-1	x_N

模型	RMSE	R	MAPE/%
BP	10.30	0.86	30.23
BP-LSTM	5.99	0.91	17.43
BP-EMD-LSTM	3.73	0.96	10.58
BP-EEMD-LSTM	2.74	0.99	8.01

Prediction of PM_2.5 Based on External Influences and Time-Series Factors

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Prediction of PM2.5 Based on External Influences and Time-Series Factors

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Prediction of PM_2.5 Based on External Influences and Time-Series Factors