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Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction
Air pollution is an important environmental issue of increasing concern, which impacts
human health. Accurate air quality prediction is crucial for avoiding people suffering from …
human health. Accurate air quality prediction is crucial for avoiding people suffering from …
Kill two birds with one stone: A multi-view multi-adversarial learning approach for joint air quality and weather prediction
Accurate and timely air quality and weather predictions are of great importance to urban
governance and human livelihood. Though many efforts have been made for air quality or …
governance and human livelihood. Though many efforts have been made for air quality or …
Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks
Accurate and timely air quality and weather predictions are of great importance to urban
governance and human livelihood. Though many efforts have been made for air quality or …
governance and human livelihood. Though many efforts have been made for air quality or …
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 …
Utilizing image transforms and diffusion models for generative modeling of short and long time series
Lately, there has been a surge in interest surrounding generative modeling of time series
data. Most existing approaches are designed either to process short sequences or to handle …
data. Most existing approaches are designed either to process short sequences or to handle …