Optimizing Long Short-Term Memory Network for Air Pollution Prediction Using a Novel Binary Chimp Optimization Algorithm

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it...

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Vydané v:	Electronics (Basel) Ročník 12; číslo 18; s. 3985
Hlavní autori:	Baniasadi, Sahba, Salehi, Reza, Soltani, Sepehr, Martín, Diego, Pourmand, Parmida, Ghafourian, Ehsan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.09.2023
Predmet:	Accuracy Air pollution Air quality Algorithms Atmospheric models Collaboration Computer architecture Distribution Environmental aspects Environmental protection Forecasting Machine learning Mathematical models Mathematical optimization Monkeys & apes Optimization Optimization algorithms Outdoor air quality Particles Performance evaluation Pollutants Prediction models Root-mean-square errors United States
ISSN:	2079-9292, 2079-9292
On-line prístup:	Získať plný text
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