Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

• Published in: Atmosphere Vol. 13; no. 6; p. 959
• Main Authors: Liu, Xiliang, Zhao, Junjie, Lin, Shaofu, Li, Jianqiang, Wang, Shaohua, Zhang, Yumin, Gao, Yuyao, Chai, Jinchuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Accuracy; Air; Air monitoring; Air pollution; Air quality; Air quality control; Bayesian analysis; Bayesian optimization; causal convolution network; Convolution; Deep learning; Feature extraction; Impact factors; individual air quality index prediction; Monitoring systems; multi-source factors; Neural networks; Nitrogen dioxide; Optimization; Outdoor air quality; Particulate matter; Pollutants; Predictions; Probability theory; Propagation; Quality control; Recurrent neural networks; Resampling; Shapley analysis; Time dependence; Time series; China

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effe...