Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China-Reference-Cited by-同舟云学术

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Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China

Published:2020-01 Issue: Volume:699 Page:133561
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Pak Unjin,Ma Jun,Ryu Unsok,Ryom Kwangchol,Juhyok U.,Pak Kyongsok,Pak Chanil

Funder

Environmental Science and Engineering Research Council, Democratic People's Republic of Korea

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference40 articles.

1. Indoor air quality analysis using deep learning with sensor data;Ahn;Sensors,2017

2. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization;Antanasijević;Sci. Total Environ.,2013

3. Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description;Baklanov;Atmos. Chem. Phys.,2008

4. Learning deep architectures for AI;Bengio;Found. Trends. Mach. Learn.,2009

5. Analysis of surface ozone using a recurrent neural network;Biancofiore;Sci. Total Environ.,2015

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