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Forecasting the Amount of Traffic-Related Pollutant Emissions by Neural Networks

Vladimir Shepelev / Ivan Slobodin / Alexandr Gritsenko / Olga Fadina

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Forecasting the Amount of Traffic-Related Pollutant Emissions by Neural Networks

Vladimir Shepelev / Ivan Slobodin / Alexandr Gritsenko / Olga Fadina

Forecasting the Amount of Traffic-Related Pollutant Emissions by Neural Networks

Vladimir Shepelev / Ivan Slobodin / Alexandr Gritsenko / Olga Fadina

Zugriff

Download

Seitennavigation

Dokumentinformationen

Ähnliche Titel

Exportieren, teilen und zitieren

Dokumentinformationen

Titel:

Forecasting the Amount of Traffic-Related Pollutant Emissions by Neural Networks

Beteiligte:

Vladimir Shepelev (Autor:in) / Ivan Slobodin (Autor:in) / Alexandr Gritsenko (Autor:in) / Olga Fadina (Autor:in)

Erschienen in:

Frontiers in Built Environment, Vol 8 (2022)

Erscheinungsdatum:

2022

ISSN:

2297-3362

DOI:

https://doi.org/10.3389/fbuil.2022.945615

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

Schlagwörter:

air pollution , particulate matter (PM 2.5) , recurrent neural networks (LSTM) , convolutional neural network (YOLOv4) , traffic , deep learning , Engineering (General). Civil engineering (General) , TA1-2040 , City planning , HT165.5-169.9

Metadata by DOAJ is licensed under CC BY-SA 1.0

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