A multi-graph spatial-temporal attention network for air-quality prediction-Reference-Cited by-同舟云学术

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A multi-graph spatial-temporal attention network for air-quality prediction

Published:2024-01 Issue: Volume:181 Page:442-451
ISSN:0957-5820
Container-title:Process Safety and Environmental Protection
language:en
Short-container-title:Process Safety and Environmental Protection

Author:

Chen Xiaoxia,Hu Yue,Dong Fangyan,Chen Kewei,Xia Hanzhong

Funder

National Natural Science Foundation of China

Ningbo University

Natural Science Foundation of Zhejiang Province

K C Wong Magna Fund in Ningbo University

Natural Science Foundation of Ningbo Municipality

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering

Reference41 articles.

1. Taking a stand against air pollution—the impact on cardiovascular disease: a joint opinion from the world heart federation, american college of cardiology, american heart association, and the European Society of Cardiology;Brauer;Circulation,2021

2. A review of artificial neural network models for ambient air pollution prediction;Cabaneros;Environ. Model. Softw.,2019

3. Deep air quality forecasting using hybrid deep learning framework;Du;IEEE Trans. Knowl. Data Eng.,2019

4. An integrated 3d cnn-gru deep learning method for short-term prediction of pm2. 5 concentration in urban environment;Faraji;Sci. Total Environ.,2022

5. Spatiotemporal informer: a new approach based on spatiotemporal embedding and attention for air quality forecasting;Feng;Environ. Pollut.,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Domain Knowledge-Enhanced Multi-spatial Multi-temporal PM2.5 Forecasting with Integrated Monitoring and Reanalysis Data;Environment International;2024-09

2. Prediction of SO2 emission concentration in industrial flue gas based on deep learning: The ammonia desulfurization system of the Yunnan aluminum carbon plant as the research object;Process Safety and Environmental Protection;2024-05

3. A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin;Entropy;2024-01-22

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