Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning

Mathematics

• Air Quality 100%
• Spatio-temporal Patterns 87%
• Forecasting 69%
• Learning 55%
• Time Series Data 29%
• Buffer 28%
• Similarity 20%
• Model 19%
• Spatial Data 16%
• Encoder 16%
• Correlated Data 15%
• Recurrent Neural Networks 15%
• Artificial Neural Network 15%
• Relationships 14%
• Moving Average 14%
• Sensor 14%
• Architecture 13%
• Baseline 13%
• Evaluate 9%
• Context 7%
• Graph in graph theory 5%

Earth & Environmental Sciences

• air quality 51%
• learning 49%
• forecasting 47%
• time series 16%
• built environment 13%
• artificial neural network 11%
• sensor 8%

Engineering & Materials Science

• Air quality 66%
• Deep learning 54%
• Time series 21%
• Recurrent neural networks 12%
• Industrial plants 11%
• Convolutional neural networks 11%
• Neural networks 7%
• Sensors 6%