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.基于此,提出基于多元经验模态分解(multiple empirical mode decomposition, MEMD)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神经网络、长短记忆神经网络(long short-term memory, LSTM)、最小二乘支持向量回归(least squares support vector regression,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,所提出的模型能够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.
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(MEMD) 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 the 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.
作者
刘金培
陈丽娟
汪漂
陈华友
LIU Jin-pei;CHEN Li-juan;WANG Piao;CHEN Hua-you(School of Business,Anhui University,Hefei 230601,China;Economics School,Anhui University,Hefei 230601,China;School of Mathematical Sciences,Anhui University,Hefei 230601,China)
出处
《控制与决策》
EI
CSCD
北大核心
2023年第2期537-545,共9页
Control and Decision
基金
国家自然科学基金项目(72071001,72001001,71871001)
教育部人文社会科学规划基金项目(20YJAZH066)
安徽省自然科学基金项目(2008085MG226,2008085QG333,2008085QG334)
安徽省高校人文社会科学研究重点项目(SK2019A0013)。
