基于VMD-CEEMD分解和LSTM的PM<sub>2.5</sub>和O<sub>3</sub>浓度预测模型

周建国; 王剑宇; 韦斯悌

doi:10.13205/j.hjgc.202306021

基于VMD-CEEMD分解和LSTM的PM_2.5和O₃浓度预测模型

doi: 10.13205/j.hjgc.202306021

华北电力大学经济管理系, 河北保定 071000

详细信息

作者简介:
周建国(1965-),男,博士,教授,主要从事大气污染治理、电厂环境技术经济分析与评价的研究。

通讯作者:
王剑宇(1998-),男,硕士,主要从事大气污染治理的研究。869487965@qq.com

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- 文章访问数: 144
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- 被引次数: 0
出版历程
- 收稿日期: 2022-09-13
- 网络出版日期: 2023-09-02

PREDICTION OF PM_2.5 AND OZONE CONCENTRATION BASED ON VMD-CEEMD DECOMPOSITION AND LSTM

School of Business Administration, North China Electric Power University, Baoding 071000, China

摘要

摘要: 针对现有PM_2.5和O₃预测模型精度不高的问题，基于南京市2015-01-01-2021-06-30期间的PM_2.5和O₃日平均浓度数据，构建了一种互补集合经验模态分解（CEEMD）二次分解和长短期记忆神经网络（LSTM）的污染物浓度预测模型。通过变分模式分解（VMD）将污染物浓度序列进行一次分解，利用分解后的残余分量进行CEEMD二次分解，再将分解后的所有子序列通过LSTM进行预测，最后将输出结果重构得到最终结果。结果表明：在南京市场景下进行预测时，与3种比较模型相比，所提出的模型具有优越性和便捷性，其O₃和PM_2.5浓度的RMSE分别为16.47、5.12。该研究结果可为分析O₃和PM_2.5污染趋势提供参考。
- 臭氧 /
- PM_2.5 /
- 浓度预测 /
- 二次分解 /
- 长短期记忆神经网络 /
- 互补集合经验模态分解
Abstract: Accurate prediction of ozone and PM_2.5 concentration can provide a scientific basis for the prevention and control of photochemical pollution. However, the prediction accuracy of the existing ozone and PM_2.5 concentration prediction models is still not sufficient. Based on the daily average ozone and PM_2.5 concentration data in Nanjing from January 1, 2015, to June 30, 2021, a pollutant concentration prediction model for complementary ensemble empirical mode decomposition (CEEMD) secondary decomposition and long and short-term memory neural network (LSTM) was constructed. Firstly, the ozone and PM_2.5 concentration sequence was decomposed by variational mode decomposition (VMD). Secondly, the CEEMD secondary decomposition was used with residual components, and then all the decomposed subsequences were predicted by LSTM. Finally, the output result was reconstructed to get the final result. The results showed that for the forcast of PM_2.5 and O₃ concentration in Nanjing, comparing with the other models, the model VMD-CEEMD-LSTM proposed in this paper was superior and robust, with the RMSE of ozone and PM_2.5 concentrations of 16.47 and 5.12, respectively. This study could provide valuable references for analyzing ozone and PM_2.5 pollution trend.
- ozone /
- PM_2.5 /
- concentration prediction /
- secondary decomposition /
- long- and short-term memory neural networks /
- CEEMD

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参考文献(24)

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