Deep learning framework for forecasting en route airspace emissions considering temporal-spatial correlation.

Wan, Junqiang; Zhang, Honghai; Zhang, Qiqian; Li, Max Z; Xu, Yan

Wan, Junqiang; Zhang, Honghai; Zhang, Qiqian; Li, Max Z; Xu, Yan.

Afiliación

Wan J; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, PR China; School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford MK43 0AL, UK. Electronic address: wanjq24@nuaa.edu.cn.
Zhang H; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, PR China. Electronic address: honghaizhang@nuaa.edu.cn.
Zhang Q; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, PR China. Electronic address: zhangqq@nuaa.edu.cn.
Li MZ; Department of Aerospace Engineering, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: maxzli@umich.edu.
Xu Y; School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford MK43 0AL, UK. Electronic address: yanxu@cranfield.ac.uk.

Sci Total Environ ; 905: 166986, 2023 Dec 20.

Article en En | MEDLINE | ID: mdl-37717749

Palabras clave

Air traffic emissions forecasting; Attention mechanism; Deep learning; Graph convolutional network; Temporalspatial correlation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos

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