Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources

Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological en...

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Materiálatiipa:	Online
Giella:	eaŋgalasgiella
Almmustuhtton:	MDPI - Multidisciplinary Digital Publishing Institute 2026
Fáttát:	Air pollution forecasting External factor optimization Genetic algorithm Autoformer Temporal variation Multivariate dependencies Relationships analysis Model interpretability OBD Emission factors COPERT Two-stream network Time-frequency features Atmospheric pollutants Wavelet decomposition Multi-layer perceptron model Atmospheric carbonyls Sampling methods Offline analytical methods Online analytical methods Problems and challenges Prospect Air pollution Ozone Houston Forecasting Quantum machine learning Quantum neural network Graph neural network Surface plasmon resonance (SPR) Particulate matter (PM) Sensors Machine learning (ML) Deep learning (DL) Artificial intelligence (AI) Environmental monitoring Air quality Deep learning WRF Weather forecasting Computational fluid dynamics Artificial intelligence Spatiotemporal convolution Air quality prediction Generative CNN Multi-scale fusion Feature correlation Digital twins Machine learning calibration Urban air pollution Graph neural networks (GNNs) N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
Liŋkkat:	ONIX_20260416T142754_9783725863303_24
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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources

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