Special Issue on Air Quality Prediction Based on Machine Learning Algorithms
Author:
Affiliation:
1. School of Information and Engineering, Dalarna University, 791 88 Falun, Sweden
2. Grupo de Biodiversidad Medio Ambiente y Salud (BIOMAS), Universidad de Las Américas UDLA, Quito 170124, Ecuador
Abstract
Publisher
MDPI AG
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Link
https://www.mdpi.com/2076-3417/13/11/6460/pdf
Reference5 articles.
1. Zainab, I., Ali, Z., Ahmad, U., Raza, S.T., Ahmad, R., Zona, Z., and Sidra, S. (2022). Air Contaminants and Atmospheric Black Carbon Association with White Sky Albedo at Hindukush Karakorum and Himalaya Glaciers. Appl. Sci., 12.
2. Zaman, N.A.F.K., Kanniah, K.D., Kaskaoutis, D.G., and Latif, M.T. (2021). Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia. Appl. Sci., 11.
3. Peralta, B., Sepúlveda, T., Nicolis, O., and Caro, L. (2022). Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks. Appl. Sci., 12.
4. Zalakeviciute, R., Rybarczyk, Y., Alexandrino, K., Bonilla-Bedoya, S., Mejia, D., Bastidas, M., and Diaz, V. (2021). Gradient Boosting Machine to Assess the Public Protest Impact on Urban Air Quality. Appl. Sci., 11.
5. Vaišis, V., Chlebnikovas, A., and Jasevičius, R. (2023). Numerical Study of the Flow of Pollutants during Air Purification, Taking into Account the Use of Eco-Friendly Material for the Filter—Mycelium. Appl. Sci., 13.
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