气候变化领域集成服务门户(CCPortal): Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism

CCPortal

DOI	10.3390/atmos15040418
	Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism
	Wang, Yumeng; Liu, Ke; He, Yuejun; Wang, Pengfei; Chen, Yuxin; Xue, Hang; Huang, Caiyi; Li, Lin
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	4
卷号	15 期号:4
英文摘要	Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019-2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants.
英文关键词	air quality prediction; GCN; LSTM; multiple attention mechanism; Qinghai-Tibet Plateau
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001210264200001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297737
作者单位	North China Institute of Aerospace Engineering; North China Institute of Aerospace Engineering
推荐引用方式 GB/T 7714	Wang, Yumeng,Liu, Ke,He, Yuejun,et al. Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism[J],2024,15(4).
APA	Wang, Yumeng.,Liu, Ke.,He, Yuejun.,Wang, Pengfei.,Chen, Yuxin.,...&Li, Lin.(2024).Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism.ATMOSPHERE,15(4).
MLA	Wang, Yumeng,et al."Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism".ATMOSPHERE 15.4(2024).