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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Vydáno v:	Environment international Ročník 175; s. 107931
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier Ltd 01.05.2023 Elsevier
Témata:	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
On-line přístup:	Získat plný text
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