Predicting air pollution in Almaty city using Deep Learning Techniques
Abstract
Keywords
Full Text:
PDFReferences
Almalawi, A., Khan, A. I., Alsolami, F., Alkhathlan, A.,
Fahad, A., Irshad, K., ... & Qaiyum, S. (2022).
Arithmetic optimization algorithm with deep learning
enabled airborne particle-bound metals size prediction
model. Chemosphere, 134960.
https://doi.org/10.1016/j.chemosphere.2022.134960
Almetwally, A. A., Bin-Jumah, M., & Allam, A. A.
(2020). Ambient air pollution and its influence on human
health and welfare: an overview. Environmental Science
and Pollution Research, 27(20), 24815-24830.
https://doi.org/10.1007/s11356-020-09042-2
Al-Janabi, S., Mohammad, M., & Al-Sultan, A. (2020).
A new method for prediction of air pollution based on
intelligent computation. Soft Computing, 24(1), 661-
https://doi.org/10.1007/s00500-019-04495-1
Asha, P., Natrayan, L. B. T. J. R. R. G. S., Geetha, B. T.,
Beulah, J. R., Sumathy, R., Varalakshmi, G., &
Neelakandan, S. (2022). IoT enabled environmental
toxicology for air pollution monitoring using AI
techniques. Environmental research, 205, 112574.
https://doi.org/10.1016/j.envres.2021.112574
Assanov, D., Zapasnyi, V., & Kerimray, A. (2021). Air
Quality and industrial emissions in the cities of
Kazakhstan. Atmosphere, 12(3), 314.
https://doi.org/10.3390/atmos12030314
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R.
(2019). A review of artificial neural network models for
ambient air pollution prediction. Environmental
Modelling & Software, 119, 285-304.
https://doi.org/10.1016/j.envsoft.2019.06.014
Gu, Y., Li, B., & Meng, Q. (2022). Hybrid interpretable
predictive machine learning model for air pollution
prediction. Neurocomputing, 468, 123-136.
https://doi.org/10.1016/j.neucom.2021.09.051
Huang, J., Na, Y., & Guo, Y. (2020). Spatiotemporal
characteristics and driving mechanism of the coupling
coordination degree of urbanization and ecological
environment in Kazakhstan. Journal of Geographical
Sciences, 30(11), 1802-1824.
https://doi.org/10.1007/s11442-020-1813-9
Janarthanan, R., Partheeban, P., Somasundaram, K., &
Elamparithi, P. N. (2021). A deep learning approach for
prediction of air quality index in a metropolitan city.
Sustainable Cities and Society, 67, 102720.
https://doi.org/10.1016/j.scs.2021.102720
Kabir, S., Islam, R. U., Hossain, M. S., & Andersson, K.
(2020). An integrated approach of belief rule base and
deep learning to predict air pollution. Sensors, 20(7),
https://doi.org/10.3390/s20071956
Karimian, H., Li, Q., Wu, C., Qi, Y., Mo, Y., Chen, G.,
& Sachdeva, S. (2019). Evaluation of different machine
learning approaches to forecasting PM2. 5 mass
concentrations. Aerosol and Air Quality Research, 19(6),
-1410. https://doi.org/10.4209/aaqr.2018.12.0450
Kim, J., & Lee, C. (2021). Deep particulate matter
forecasting model using correntropy-induced loss.
Journal of Mechanical Science and Technology, 35(9),
-4063. https://doi.org/10.1007/s12206-021-0817-4
Minh, V. T. T., Tin, T. T., & Hien, T. T. (2021). PM2. 5
Forecast System by Using Machine Learning and WRF
Model, A Case Study: Ho Chi Minh City, Vietnam.
Aerosol and Air Quality Research, 21, 210108.
https://doi.org/10.4209/aaqr.210108
Özkaynak, H., Baxter, L. K., Dionisio, K. L., & Burke,
J. (2013). Air pollution exposure prediction approaches
used in air pollution epidemiology studies. Journal of
exposure science & environmental epidemiology, 23(6),
-572. https://doi.org/10.1038/jes.2013.15
Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K.,
& Pak, C. (2020). Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A
case study of Beijing, China. Science of The Total
Environment, 699, 133561.
https://doi.org/10.1016/j.scitotenv.2019.07.367
Shang, Z., Deng, T., He, J., & Duan, X. (2019). A novel
model for hourly PM2. 5 concentration prediction based
on CART and EELM. Science of The Total
Environment, 651, 3043-3052.
https://doi.org/10.1016/j.scitotenv.2018.10.193
Tadano, Y. S., Potgieter-Vermaak, S., Kachba, Y. R.,
Chiroli, D. M., Casacio, L., Santos-Silva, J. C., ... &
Godoi, R. H. (2021). Dynamic model to predict the
association between air quality, COVID-19 cases, and
level of lockdown. Environmental Pollution, 268,
https://doi.org/10.1016/j.envpol.2020.115920
Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., &
Chi, T. (2019). A novel spatiotemporal convolutional
long short-term neural network for air pollution
prediction. Science of the total environment, 654, 1091-
https://doi.org/10.1016/j.scitotenv.2018.11.086
Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., &
Talebiesfandarani, S. (2019). PM2. 5 prediction based on
random forest, XGBoost, and deep learning using
multisource remote sensing data. Atmosphere, 10(7),
https://doi.org/10.3390/atmos10070373
Zhao, R., Gu, X., Xue, B., Zhang, J., & Ren, W. (2018).
Short period PM2. 5 prediction based on multivariate
linear regression model. PloS one, 13(7), e0201011.
https://doi.org/10.1371/journal.pone.0201011
Zeinalnezhad, M., Chofreh, A. G., Goni, F. A., &
Klemeš, J. J. (2020). Air pollution prediction using semiexperimental regression model and Adaptive NeuroFuzzy Inference System. Journal of Cleaner Production,
, 121218.
https://doi.org/10.1016/j.jclepro.2020.121218
DOI: http://dx.doi.org/10.21533/scjournal.v11i2.233
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Assel Nurlybayeva, Ali Abd Almisreb, Nooritawati Md Tahir
ISSN 2233 -1859
Digital Object Identifier DOI: 10.21533/scjournal
This work is licensed under a Creative Commons Attribution 4.0 International License