Yang Hong, Zhao Junlin, Li Guohui
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
Environ Sci Pollut Res Int. 2022 Sep;29(44):67214-67241. doi: 10.1007/s11356-022-20375-y. Epub 2022 May 6.
As air pollution worsens, the prediction of PM concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM concentration accurately. PM concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.
随着空气污染的加剧，颗粒物（PM）浓度预测对于公众健康变得愈发重要。本文提出了一种基于自适应噪声完备总体经验模态分解（CEEMDAN）、幅度感知排列熵（AAPE）、基于海洋捕食者算法改进的变分模态分解（MPA-VMD）以及基于黑猩猩优化算法优化的极限学习机（ChOA-ELM）的新型PM浓度混合预测模型，命名为CEEMDAN-AAPE-MPA-VMD-ChOA-ELM。首先，使用CEEMDAN对原始数据进行分解，并用AAPE对所有固有模态函数（IMF）分量的复杂性进行量化。其次，使用MPA-VMD对具有最大AAPE的IMF分量进行分解。最后，使用ChOA-ELM对所有IMF分量进行预测，并将所有预测结果进行重构以获得最终预测结果。所提出的模型结合了二次分解技术、特征分析和优化算法的优点，能够准确预测PM浓度。使用2021年3月1日至2021年3月31日在中国上海和沈阳每小时采集的PM浓度数据进行实验研究和DM检验。上海的实验结果表明，所提出模型的均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和相关系数（R）分别为1.0676、0.7685、0.0181和0.9980，在90%置信水平下优于所有对比模型。在沈阳，所提出模型的RMSE、MAE、MAPE和R分别为1.4399、1.1258、0.0389和0.9976，在95%置信水平下优于所有对比模型。
