Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

[Display omitted] •High-resolution prediction of air fine particular matter concentration is conducted.•New developed machine learning algorithms are adopted for this purpose.•Prediction for temporal/spatial variability of PM2.5 over Iraq region is studied.•Research provided good forecasting spatial...

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Bibliographic Details
Published in:	Environment international Vol. 175; p. 107931
Main Authors:	Tao, Hai, Jawad, Ali H., Shather, A.H., Al-Khafaji, Zainab, Rashid, Tarik A., Ali, Mumtaz, Al-Ansari, Nadhir, Marhoon, Haydar Abdulameer, Shahid, Shamsuddin, Yaseen, Zaher Mundher
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Ltd 01.05.2023 Elsevier
Subjects:	air Air Pollutants - analysis air pollution Air Pollution - analysis Air quality prediction Algorithms Arid climate Bayes Theorem Bayesian theory environment Environmental Monitoring - methods Geoteknik human health Humans Iraq Machine Learning neural networks Particulate Matter - analysis PM2.5 concentration prediction rain relative humidity Simulated annealing Soil Mechanics soil water summer temperature wind speed Iraq Arid climate Air quality prediction PM2.5 concentration Simulated annealing Machine learning PM(2.5) concentration
ISSN:	0160-4120, 1873-6750, 1873-6750
Online Access:	Get full text
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