Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru
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Fecha
2021-12
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
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Título del volumen
Editor
Nature Research
Nombre de Curso
Licencia CC
Atribution 4.0 International (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
The prediction of air pollution is of great importance in highly populated areas because it directly
impacts both the management of the city’s economic activity and the health of its inhabitants. This
work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru,
using artifcial neural networks. The conventional feedforward backpropagation known as Multilayer
Perceptron (MLP) and the Recurrent Artifcial Neural network known as Long Short-Term Memory
networks (LSTM) were implemented for the hourly prediction of PM10 based on the past values of
this pollutant and three meteorological variables obtained from fve monitoring stations. The models
were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV).
The simulation results show that periods of moderate PM10 concentration are predicted with high
precision. Whereas, for periods of high contamination, the performance of both models, the MLP and
LSTM, were diminished. On the other hand, the prediction performance improved slightly when the
models were trained and validated with the BNCV scheme. The simulation results showed that the
models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized
by a moderate to low level of contamination. However, the results show the difculty of predicting
this contaminant in those stations that present critical contamination episodes, such as ATE and HCH.
In conclusion, the LSTM recurrent artifcial neural networks with BNCV adapt more precisely to critical
pollution episodes and have better predictability performance for this type of environmental data.
Notas
Indexación Scopus.
Palabras clave
Environmental Sciences, Environmental Scial Sciences, Air Pollution, Behavior of Air
Citación
Scientific Reports. Volume 11, Issue 1. December 2021. Article number 24232
DOI
10.1038/s41598-021-03650-9