Air quality prediction using CNN+LSTM-based hybrid deep learning architecture-Reference-Cited by-同舟云学术

Air quality prediction using CNN+LSTM-based hybrid deep learning architecture

Published:2021-09-23 Issue:8 Volume:29 Page:11920-11938
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Gilik Aysenur^ORCID,Ogrenci Arif Selcuk,Ozmen Atilla

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-021-16227-w.pdf

Reference43 articles.

1. Aceves-Fernandez M, Domínguez-Guevara R, Pedraza Ortega J C, Vargas-Soto J (2020) Evaluation of key parameters using deep convolutional neural networks for airborne pollution (pm10) prediction. Discret Dyn Nat Soc 2020:1–14. https://doi.org/10.1155/2020/2792481

2. Al-Janabi S, Mohammad M, Al-Sultan A (2019) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24:661–680. https://doi.org/10.1007/s00500-019-04495-1

3. Al-Janabi S, Alkaim A, Al-Janabi E, A Aljeboree MM (2021) Intelligent forecaster of concentrations (pm2.5, pm10, no2, co, o3, so2) caused air pollution (ifcsap). Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06067-7

4. Bakici T, Almirall E, Wareham J (2012) A smart city initiative: The case of barcelona. J Knowl Econ:4. https://doi.org/10.1007/s13132-012-0084-9

5. Barcelona City Council (2020) Open data bcn. https://opendata-ajuntament.barcelona.cat/en/https://opendata-ajuntament.barcelona.cat/en/, (last accessed: 15.04.2021)

Cited by 64 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. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review;Remote Sensing;2024-09-11

2. Characterization and prediction of PM2.5 levels in Afghanistan using machine learning techniques;Theoretical and Applied Climatology;2024-09-06

3. Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments;Sustainability;2024-08-17

4. Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting;ICST Transactions on Scalable Information Systems;2024-07-11

5. Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks;ITISE 2024;2024-07-04