RT Journal Article
T1 Spatial regression analysis of NO_(x) and O_(3) concentrations in Madrid urban area using Radial Basis Function networks
A1 Salcedo Sanz, S.
A1 Portilla Figueras, J.A.
A1 Ortiz Garcíaq, E.G.
A1 Pérez Bellido, A.M.
A1 García Herrera, Ricardo
A1 Elorrieta, J.I.
AB This paper discusses the performance of Radial Basis Function networks (RBF) in a problem of spatial regression of pollutants in Madrid. Specifically, the spatial regression of NO_(x) and O_(3) is considered, in such a way that, starting from a set of measuring points provided by the air quality monitoring network of Madrid, the complete surface of the pollutants in the city is obtained. This pollutant surface can be used as an initial step for modeling intra-urban pollution using land-use regression techniques for example. Also, different works has used a pollutant surface to study the patterns of pollution in different cities in the world and also to establish their air monitoring networks under mathematical criteria. The paper is focussed in analyzing the performance of RBF networks to obtain this first pollutant surface, so different RBF training algorithms are tested in this paper. Specifically, evolutionary-based RBF training algorithms are described, and compared with classical training algorithms for RBF networks with Gaussian kernels. The inclusion of meteorological variables in the RBF networks are also discussed in the paper. The experimental part of the article studies real results of the application of RBF networks to obtain a first pollutant surface of NO_(x) and O_(3), using the data of the air pollution monitoring network of Madrid and the meteorological network of the city.
PB Elsevier Science BV
SN 0169-7439
YR 2009
FD 2009-11-15
LK https://hdl.handle.net/20.500.14352/43028
UL https://hdl.handle.net/20.500.14352/43028
LA eng
NO © 2009 Elsevier B.V. All rights reserved. The authors would like to thank the Air Quality Area of Madrid City Council for their support in this investigation. This work has been partially supported by Comunidad de Madrid, Universidad de Alcalá, through Project CCG07-UAH/AMB-3993. E. G. Ortiz-García is supported by Universidad de Alcalá through a FPI grant. Á. M. PérezBellido is supported by a doctoral fellowship by the European Social Fund and Junta de Comunidades de Castilla la Mancha, in the frame of the Operating Programme ESF 2007–2013.
NO Comunidad de Madrid/Universidad de Alcalá
NO Junta de Comunidades de Castilla la Mancha
NO Air Quality Area of Madrid City Council
NO European Social Fund
DS Docta Complutense
RD 28 abr 2024