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dc.contributor.advisorLópez Gonzales, Javier Linkolk
dc.contributor.authorHoyos Cordova, Chardin
dc.contributor.authorLopez Portocarrero, Manuel Niño
dc.date.accessioned2021-10-12T17:33:46Z
dc.date.available2021-10-12T17:33:46Z
dc.date.issued2021-08-23
dc.identifier.urihttp://hdl.handle.net/20.500.12840/4837
dc.description.abstractThe prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city’s economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.en_ES
dc.formatapplication/pdfen_ES
dc.language.isoeng
dc.publisherUniversidad Peruana Uniónen_ES
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_ES
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 Spain*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/*
dc.subjectAir pollutionen_ES
dc.subjectAir qualityen_ES
dc.subjectRecurrent artificial neural networksen_ES
dc.subjectTime-series forecastingen_ES
dc.titleEvaluación de la calidad del aire y predicción de la contaminación utilizando redes neuronales artificiales recurrentes en Lima Metropolitana-Perúen_ES
dc.title.alternativeAir quality assessmentand pollution forecasting using recurrent artificial neural networks in Metropolitan Lima-Peruen_ES
dc.typeinfo:eu-repo/semantics/bachelorThesisen_ES
thesis.degree.disciplineIngeniería Ambientalen_ES
thesis.degree.grantorUniversidad Peruana Unión. Facultad de Ingeniería y Arquitecturaen_ES
thesis.degree.nameIngeniero Ambientalen_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#2.07.00en_ES
dc.description.sedeLIMAen_ES
dc.description.escuelaEscuela Profesional de Ingeniería Ambientalen_ES
dc.description.lineadeinvestigacionGestión Ambientalen_ES
renati.advisor.dni46071566
renati.advisor.orcidhttps://orcid.org/0000-0003-0847-0552en_ES
renati.author.dni71314692
renati.author.dni75588871
renati.discipline521066en_ES
renati.jurorCruz Huaranga, Milda Amparo
renati.jurorCurasi Rafael, Nancy
renati.jurorFernández Rojas, Joel Hugo
renati.jurorPérez Carpio, Jackson Edgardo
renati.levelhttp://purl.org/pe-repo/renati/nivel#tituloProfesionalen_ES
renati.typehttp://purl.org/pe-repo/renati/type#tesisen_ES
dc.date.embargoEnd2022-08-23
dc.publisher.countryPEen_ES
dc.publisher.countryPE


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