基于MEMD和空间层次聚类的PM2.5三角模糊序列多因子组合预测研究
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作者单位:

安徽大学

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中图分类号:

O 212

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Multi-factor Combination Prediction of PM2.5 Triangular Fuzzy Series Based on MEMD and Spatial Hierarchical Clustering
Author:
Affiliation:

Anhui University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    日间PM2.5浓度受本地和邻近地区的多重因素影响,具有高度不确定性和不稳定性。常见的PM2.5实值序列和区间序列分别反映其日均和极值波动状况,三角模糊序列将两者优点相结合可包含更多的有效信息。因此本文提出基于多元经验模态分解和空间层次聚类的PM2.5 三角模糊序列多因子组合预测模型。首先,运用皮尔曼相关系数分析PM2.5与本地污染物浓度、气象要素间的关联度,选取本地影响因子;其次,计算PM2.5与空间污染物浓度间的关联度,并据此对邻近城市K-means空间聚类得到核心影响、一般影响和偏远影响城市群,并统计各城市群不同污染物的综合指数,即空间影响因子;进而,利用MEMD对PM2.5和影响因子的三角模糊序列同时进行分解,重构得到高频、低频以及趋势序列;最后,运用BP、LSTM、LSSVR分别对子序列进行多输入单输出的预测,并将上述单项预测结果相加,即得到PM2.5三角模糊序列的预测值。仿真实验结果表明,本文提出的模型能充分考虑气象条件和多种污染物的空间影响,具有较强的预测精度和良好的实用性。

    Abstract:

    Daily PM2.5 concentration is very uncertain and unstable due to multiple factors of local and adjacent areas. The common PM2.5 real-valued and interval series reflect the daily and extreme value fluctuations respectively, while triangular fuzzy series combined with both advantages and include more effective information. Therefore, this paper proposes a multi-factor combination forecasting model of PM2.5 triangular fuzzy series based on Multiple Empirical Mode Decomposition and spatial hierarchical clustering. Firstly, the Pearson correlation coefficient is used to analyze the correlation between PM2.5 concentration and local pollutant concentration and meteorological elements, so as to select the local impact factors. Secondly, the Pearson correlation between PM2.5 and spatial pollutant concentration is calculated. Based on this, we obtain the urban agglomerations with core impact, general impact and remote impact by the K-means spatial clustering of neighboring cities. Thirdly, The comprehensive index of different pollutants in each urban agglomeration is obtained, which is used as the spatial impact factors. Then, the triangular fuzzy sequences of PM2.5 and influence factors are decomposed simultaneously by MEMD, and the high frequency, low frequency and trend sequences are reconstructed. Finally, BP, LSTM and LSSVR are employed to predict the subsequences respectively, and the predicted value of PM2.5 triangular fuzzy series is obtained by adding the above single prediction results. The simulation results show that the proposed model can effectively utilize the meteorological conditions and the spatial effects of various pollutants, and has strong predictive performance and good practicability.

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历史
  • 收稿日期:2021-07-04
  • 最后修改日期:2021-10-10
  • 录用日期:2021-10-27
  • 在线发布日期: 2021-12-01
  • 出版日期: