Using Deep Transformer Based Models to Predict Ozone Levels-Reference-Cited by-同舟云学术

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Using Deep Transformer Based Models to Predict Ozone Levels

Published:2022 Issue: Volume: Page:169-182
ISSN:0302-9743
Container-title:Intelligent Information and Database Systems
language:
Short-container-title:

Author:

Méndez Manuel,Montero Carlos,Núñez Manuel^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21743-2_14

Reference24 articles.

1. AEMET Open Data. https://opendata.aemet.es/centrodedescargas/productosAEMET?. Accessed 25 Oct 2021

2. City of Madrid. https://www.mambiente.madrid.es/opencms/export/sites/default/calaire/Anexos/Procedimiento_ozono.pdf. Accessed 20 Oct 2021

3. Netron open source tool. https://netron.app/. Accessed 15 Nov 2021

4. New South Wales Government. https://www.health.nsw.gov.au/environment/air/Pages/common-air-pollutants.aspx. Accessed 20 Oct 2021

5. World Health Organisation. https://www.who.int/health-topics/air-pollution. Accessed 20 Oct 2021

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1. From data to action in flood forecasting leveraging graph neural networks and digital twin visualization;Scientific Reports;2024-08-10

2. Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications;Archives of Computational Methods in Engineering;2023-06-13

3. Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model;Engineering Applications of Artificial Intelligence;2023-05

4. Machine learning algorithms to forecast air quality: a survey;Artificial Intelligence Review;2023-02-16

5. Forecasting $$\text {PM}_{2.5}$$ Concentrations in Ambient Air Using a Transformer Based Neural Network;Information and Communication Technologies;2023

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