Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN)...

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Bibliographic Details
Main Authors:	Julio Alberto Ramírez-Montañez, Marco Antonio Aceves-Fernández, Jesús Carlos Pedraza-Ortega, Efrén Gorrostieta-Hurtado, Artemio Sotomayor-Olmedo
Format:	Article
Language:	English
Published:	MDPI AG 2021-12-01
Series:	Applied Sciences
Subjects:	recurrent neural network long short-term memory (LSTM) gate recurrent unit (GRU) Elman neural network particle matter air pollution
Online Access:	https://www.mdpi.com/2076-3417/12/1/256

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