An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM<sub>2.5</sub> concentrations across China.

Chen, Binjie; You, Shixue; Ye, Yang; Fu, Yongyong; Ye, Ziran; Deng, Jinsong; Wang, Ke; Hong, Yang

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM_2.5 concentrations across China.

Chen, Binjie; You, Shixue; Ye, Yang; Fu, Yongyong; Ye, Ziran; Deng, Jinsong; Wang, Ke; Hong, Yang.

Afiliación

Chen B; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
You S; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Ye Y; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Fu Y; College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
Ye Z; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Deng J; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: jsong_deng@zju.edu.cn.
Wang K; College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Hong Y; School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK 73019, USA.

Sci Total Environ ; 768: 144724, 2021 May 10.

Article en En | MEDLINE | ID: mdl-33434807

Palabras clave

Aerosol optical depth (AOD); Attention module; Deep learning; Gap-filling; Particulate matter; Predictor importance

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Total Environ Año: 2021 Tipo del documento: Article País de afiliación: China

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