A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hy...

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Vydáno v:	The Science of the total environment Ročník 709; s. 135934
Hlavní autoři:	Sharma, Ekta, Deo, Ravinesh C., Prasad, Ramendra, Parisi, Alfio V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 20.03.2020
Témata:	air quality algorithms Artificial intelligence atmospheric visibility autocorrelation health care costs ICEEMDAN monitoring mortality Particulate matter (PM2.5, PM10) particulates public health Real-time air quality forecasts risk reduction trees Visibility r2 VIS D FDMS μg/m3 ELM Qld ENS NEPH NEPM SD D-TEOM WI MLR CHAG PM2.5 CEEMDAN EEMD BMA ICEEMDAN EMD WHO DERM PM2.5, iFOR AI OS-ELM TEOM RMSE Real-time air quality forecasts ppm BAM AQ MAE μm PACF r PM2.5, iOBS OEH IMFs NSW PM10 Particulate matter (PM2.5, PM10) Visibility DoE Artificial intelligence Particulate matter (PM(2.5), PM)
ISSN:	0048-9697, 1879-1026, 1879-1026
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