High granular and short term time series forecasting of $$\hbox {PM}_{2.5}$$ air pollutant - a comparative review-Reference-Cited by-同舟云学术

High granular and short term time series forecasting of $$\hbox {PM}_{2.5}$$ air pollutant - a comparative review

Published:2021-03-29 Issue: Volume: Page:
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Das Rituparna,Middya Asif Iqbal,Roy Sarbani^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

http://link.springer.com/content/pdf/10.1007/s10462-021-09991-1.pdf

Reference46 articles.

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3. Bose R, Dey RK, Roy S, Sarddar D (2020) Time series forecasting using double exponential smoothing for predicting the major ambient air pollutants. Information and communication technology for sustainable development. Springer, Singapore, pp 603–613

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5. Brunelli U, Piazza V, Pignato L, Sorbello F, Vitabile S (2007) Two-days ahead prediction of daily maximum concentrations of $$\text{ SO}_{2}$$, $$\text{ O}_{3}$$, $$\text{ PM}_{10}$$, $$\text{ NO}_{2}$$, CO in the urban area of Palermo, Italy. Atmos Environ 41(14):2967–2995

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