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Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm | |
Wu, Yang1,2; Qian, Chonghui1,2; Huang, Hengjun1,2 | |
2024-07 | |
发表期刊 | ENTROPY |
卷号 | 26期号:7 |
摘要 | Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction. |
关键词 | air quality index (AQI) Variational Mode Decomposition dung beetle optimization Informer Convolutional Neural Network-Long Short Term Memory |
DOI | 10.3390/e26070534 |
收录类别 | SCIE |
语种 | 英语 |
WOS研究方向 | Physics |
WOS类目 | Physics, Multidisciplinary |
WOS记录号 | WOS:001277477600001 |
出版者 | MDPI |
原始文献类型 | Article |
EISSN | 1099-4300 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.lzufe.edu.cn/handle/39EH0E1M/37599 |
专题 | 统计与数据科学学院 |
通讯作者 | Huang, Hengjun |
作者单位 | 1.Lanzhou Univ Finance & Econ, Sch Stat & Data Sci, Lanzhou 730020, Peoples R China; 2.Key Lab Digital Econ & Social Comp Sci Gansu, Lanzhou 730020, Peoples R China |
第一作者单位 | 兰州财经大学 |
通讯作者单位 | 兰州财经大学 |
推荐引用方式 GB/T 7714 | Wu, Yang,Qian, Chonghui,Huang, Hengjun. Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm[J]. ENTROPY,2024,26(7). |
APA | Wu, Yang,Qian, Chonghui,&Huang, Hengjun.(2024).Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm.ENTROPY,26(7). |
MLA | Wu, Yang,et al."Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm".ENTROPY 26.7(2024). |
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