Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach-Reference-Cited by-同舟云学术

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Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach

Published:2020-04 Issue: Volume:253 Page:119841
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Wang Hong-Wei,Li Xiao-Bing,Wang Dongsheng,Zhao Juanhao,He Hong-di,Peng Zhong-Ren

Funder

National Planning Office of Philosophy and Social Science

National Natural Science Foundation of China

China Scholarship Council

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference38 articles.

1. A generalized kruskal-wallis test incorporating group uncertainty with application to genetic association studies;Acar;Biometrics,2013

2. Modelling carbon emission intensity: application of artificial neural network;Acheampong;J. Clean. Prod.,2019

3. DeepAirNet: applying recurrent networks for air quality prediction;Athira;Procedia Computer Science,2018

4. Multivariate methods to predict ground level ozone during daytime, nighttime, and critical conversion time in urban areas;Awang;Atmos.Pollut. Res.,2015

5. Neural machine translation by jointly learning to align and translate;Bahdanau,2015

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