Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO<sub>2</sub> (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO<sub>2</sub> (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

This study aims to produce accurate predictions of the NO<sub>2</sub> concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to crea...

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Bibliographic Details
Main Authors: Javier González-Enrique, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso-López, Daniel Urda, Lipika Deka, Ignacio J. Turias
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Sensors
Subjects:
forecasting
feature selection
air pollution
nitrogen dioxide
artificial neural networks
LSTMs
Online Access:https://www.mdpi.com/1424-8220/21/5/1770

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