Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

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dc.contributor.authorHoyos Cordova, Chardin
dc.contributor.authorLopez Portocarrero, Manuel Niño
dc.contributor.authorSalas, Rodrigo
dc.contributor.authorTorres, Romina
dc.contributor.authorCanas Rodrigues, Paulo
dc.contributor.authorLópez-Gonzales, Javier Linkolk
dc.date.accessioned2023-06-15T20:40:58Z
dc.date.available2023-06-15T20:40:58Z
dc.date.issued2021-12
dc.descriptionIndexación Scopus.es
dc.description.abstractThe 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.es
dc.description.urihttps://www.nature.com/articles/s41598-021-03650-9
dc.identifier.citationScientific Reports. Volume 11, Issue 1. December 2021. Article number 24232es
dc.identifier.doi10.1038/s41598-021-03650-9
dc.identifier.issn2045-2322
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/50768
dc.language.isoenes
dc.publisherNature Researches
dc.rights.licenseAtribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectEnvironmental Scienceses
dc.subjectEnvironmental Scial Scienceses
dc.subjectAir Pollutiones
dc.subjectBehavior of Aires
dc.titleAir quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Perues
dc.typeArtículoes
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