气候变化领域集成服务门户(CCPortal): Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau

CCPortal

DOI	10.3390/atmos14121821
	Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau
	Wang, Yumeng; Liu, Ke; He, Yuejun; Fu, Qiming; Luo, Wei; Li, Wentao; Liu, Xuan; Wang, Pengfei; Xiao, Siyuan
发表日期	2023
EISSN	2073-4433
卷号	14 期号:12
英文摘要	In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R2) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains.
关键词	deep learning missing value imputation data validity air quality Qinghai-Tibet Plateau
英文关键词	INTERPOLATION; REGRESSION
WOS研究方向	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001131235000001
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/283419
作者单位	North China Institute of Aerospace Engineering
推荐引用方式 GB/T 7714	Wang, Yumeng,Liu, Ke,He, Yuejun,et al. Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau[J],2023,14(12).
APA	Wang, Yumeng.,Liu, Ke.,He, Yuejun.,Fu, Qiming.,Luo, Wei.,...&Xiao, Siyuan.(2023).Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau.ATMOSPHERE,14(12).
MLA	Wang, Yumeng,et al."Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau".ATMOSPHERE 14.12(2023).