A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network-Reference-Cited by-同舟云学术

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A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network

Published:2021-10 Issue:10 Volume:12 Page:101197
ISSN:1309-1042
Container-title:Atmospheric Pollution Research
language:en
Short-container-title:Atmospheric Pollution Research

Author:

Liu Xinwei,Qin Muchuan,He Yue,Mi Xiwei^ORCID,Yu Chengqing^ORCID

Funder

China Postdoctoral Science Foundation

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Atmospheric Science,Pollution,Waste Management and Disposal

Reference64 articles.

1. Hierarchical traffic signal optimization using reinforcement learning and traffic prediction with long-short term memory;Abdoos;Expert Syst. Appl.,2021

2. Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality;Bai;J. Clean. Prod.,2019

3. An ensemble long short-term memory neural network for hourly PM2. 5 concentration forecasting;Bai;Chemosphere,2019

4. Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks;Becerra-Rico;Earth Sci. India,2020

5. Deep convolutional neural networks for Bearings failure predictionand temperature correlation;Belmiloud;Journal of Vibroengineering,2018

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