Clustering approach applied on an artificial neural network model to predict PM10 in mega cities of México

Vista sencilla de ítem
dc.contributor.authorMagaña-Villegas, Elizabeth
dc.contributor.authorCarrera-Velueta, Jesús M.
dc.contributor.authorRamos-Herrera, Sergio
dc.contributor.authorHernández-Barajas, José R.
dc.contributor.authorGonzález-Figueredo, Carlos
dc.contributor.authorLaines-Canepa, José R.
dc.contributor.authorValdés-Manzanilla, Arturo
dc.contributor.authorBautista-Margulis, Raúl G.
dc.date.accessioned2018-05-08T21:02:27Z
dc.date.available2018-05-08T21:02:27Z
dc.date.issued2016
dc.descriptionA cluster-based artificial neural network model called CLASO (Classification-Assemblage-Association) has been proposed to predict the maximum of the 24-h moving average of PM10 concentration on the next day in the three largest metropolitan areas of Mexico. The model is a self-organised, real-time learning neural network, which builds its topology via a process of pattern classification by using an historical database. This process is based on a supervised clustering technique, assigning a class to each centroid of the hidden layer, employing the Euclidean distance as a hierarchical criterion. A set of ARIMA models was compared with CLASO model in the forecast performance of the 24-h average PM10 concentration on the next day. In general, CLASO model produced more accurate predictions of the maximum of the 24-h moving average of PM10 concentration than the ARIMA models, although the latter showed a minor tendency to underpredict the results. The CLASO model solely requires to be built a historical database of the air quality parameter, an initial radius of classification and the learning factor. CLASO has demonstrated acceptable predictions of 24-h average PM10 concentration by using exclusively regressive PM10 concentrations. The forecasting capabilities of the model were found to be satisfactory compared to the classical models, demonstrating its potential application to the other major pollutants used in the Mexican air quality index.es
dc.identifier.citationMagaña-Villegas, E.; Carrera-Velueta, J.M.; Ramos-Herrera, S.; Hernández-Barajas, J.R.; González-Figueredo, C.; Laines-Canepa, J.R.; Valdés-Manzanilla, A.; Bautista-Margulis, R.G. (2016). Clustering approach applied on an artificial neural network model to predict PM10 in mega cities of México. International Journal of Sustainable Development and Planning 11(4). Inglaterra: Wit Press. DOI: 10.2495/SDP-V11-N4-566-577es
dc.identifier.issn1743-761X
dc.identifier.urihttp://hdl.handle.net/11117/5324
dc.language.isoenges
dc.publisherWIT Presses
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectAir Qualityes
dc.subjectArtificial Neural Networkes
dc.subjectClusteringes
dc.subjectCalidad del Airees
dc.titleClustering approach applied on an artificial neural network model to predict PM10 in mega cities of Méxicoes
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisorInternational Journal of Sustainable Development and Planning

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Clustering approach applied on an artificial neural network model to predict PM10 in mega cities of México.pdf
Tamaño:
1.11 MB
Formato:
Adobe Portable Document Format
Descargar

Colecciones

DPTI - Artículos y ponencias con arbitraje