A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Attention based spatiotemporal graph attention networks for traffic flow forecasting
Traffic flow forecasting is a crucial task in transportation and necessary for congestion
mitigation, traffic control, and intelligent traffic management. Deep learning models can aid …
mitigation, traffic control, and intelligent traffic management. Deep learning models can aid …
A new multi-data-driven spatiotemporal PM2. 5 forecasting model based on an ensemble graph reinforcement learning convolutional network
X Liu, M Qin, Y He, X Mi, C Yu - Atmospheric Pollution Research, 2021 - Elsevier
Spatiotemporal PM2. 5 forecasting technology plays an important role in urban traffic
environment management and planning. In order to establish a satisfactory high-precision …
environment management and planning. In order to establish a satisfactory high-precision …
Spatiotemporal air quality forecasting and health risk assessment over smart city of NEOM
Modeling and predicting air pollution concentrations is important to provide early warnings
about harmful atmospheric substances. However, uncertainty in the dynamic process and …
about harmful atmospheric substances. However, uncertainty in the dynamic process and …
Deep neural networks for spatiotemporal PM2. 5 forecasts based on atmospheric chemical transport model output and monitoring data
Abstract Reliable long-horizon PM 2.5 forecasts are crucial and beneficial for health
protection through early warning against air pollution. However, the dynamic nature of air …
protection through early warning against air pollution. However, the dynamic nature of air …
A new ensemble spatio-temporal PM2. 5 prediction method based on graph attention recursive networks and reinforcement learning
Inhalable particulate matter with a diameter of less than 2.5 μm spatio-temporal prediction
technology is an important tool for environmental governance in urban traffic congestion …
technology is an important tool for environmental governance in urban traffic congestion …
A dual-path dynamic directed graph convolutional network for air quality prediction
Accurate air quality prediction can help cope with air pollution and improve the life quality.
With the development of the deployments of low-cost air quality sensors, increasing data …
With the development of the deployments of low-cost air quality sensors, increasing data …
Deciphering urban traffic impacts on air quality by deep learning and emission inventory
Air pollution is a major obstacle to future sustainability, and traffic pollution has become a
large drag on the sustainable developments of future metropolises. Here, combined with the …
large drag on the sustainable developments of future metropolises. Here, combined with the …