Machine learning algorithms to forecast air quality: a survey
Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is
important to develop forecasting mechanisms that can be used by the authorities, so that …
important to develop forecasting mechanisms that can be used by the authorities, so that …
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network
Predicting air quality in fine spatiotemporal granularity is of great importance for air pollution
control and urban sustainability. However, existing studies are either focused on predicting …
control and urban sustainability. However, existing studies are either focused on predicting …
Deep-AIR: A hybrid CNN-LSTM framework for fine-grained air pollution estimation and forecast in metropolitan cities
Air pollution presents a serious health challenge in urban metropolises. While accurately
monitoring and forecasting air pollution are highly crucial, existing data-driven models have …
monitoring and forecasting air pollution are highly crucial, existing data-driven models have …
An air quality prediction model based on improved Vanilla LSTM with multichannel input and multiroute output
W Fang, R Zhu, JCW Lin - Expert systems with applications, 2023 - Elsevier
Long short-term memory (LSTM), especially vanilla LSTM (VLSTM), has been widely used in
air quality prediction field. However, VLSTM has many more parameters, thereby making …
air quality prediction field. However, VLSTM has many more parameters, thereby making …
Machine learning for urban air quality analytics: A survey
The increasing air pollution poses an urgent global concern with far-reaching
consequences, such as premature mortality and reduced crop yield, which significantly …
consequences, such as premature mortality and reduced crop yield, which significantly …
Visualizing large-scale spatial time series with geochron
In geo-related fields such as urban informatics, atmospheric science, and geography, large-
scale spatial time (ST) series (ie, geo-referred time series) are collected for monitoring and …
scale spatial time (ST) series (ie, geo-referred time series) are collected for monitoring and …
ASTGC: Attention-based spatio-temporal fusion graph convolution model for fine-grained air quality analysis
Y Zhao, S Fan, K Xia, Y Jia, L Wang, W Yang - Air Quality, Atmosphere & …, 2023 - Springer
The deployment of air quality monitoring stations is limited in number and unevenly
distributed, resulting in a limited number of collected samples, so fine-grained analysis of air …
distributed, resulting in a limited number of collected samples, so fine-grained analysis of air …
An intelligent air monitoring system for pollution prediction: a predictive healthcare perspective
V Behal, R Singh - The Computer Journal, 2024 - academic.oup.com
The extensive potential of Internet of Things (IoT) technology has enabled the widespread
real-time perception and analysis of health conditions. Furthermore, the integration of IoT in …
real-time perception and analysis of health conditions. Furthermore, the integration of IoT in …
Navier–stokes Generative Adversarial Network: a physics-informed deep learning model for fluid flow generation
P Wu, K Pan, L Ji, S Gong, W Feng, W Yuan… - Neural Computing and …, 2022 - Springer
Abstract Numerical simulation in Computational Fluid Dynamics mainly relies on discretizing
the governing equations in time or space to obtain numerical solutions, which is expensive …
the governing equations in time or space to obtain numerical solutions, which is expensive …