Soft computing models to identify typical meteorological days

Soft computing models are capable of identifying patterns that can characterize a ‘typical day’ in terms of its meteorological conditions. This multidisciplinary study examines data on six meteorological parameters gathered in a Spanish city. Data on these and other variables were collected for over 6 months, in 2007, from a pollution measurement station that forms part of a network of similar stations in the Spanish Autonomous Region of Castile– Leon. A comparison of the meteorological data allows relationships to be established between the meteorological variables and the days of the year. One of the main contributions of this study is the selection of appropriate data processing techniques, in order to identify typical days by analysing meteorological variables and aerosol pollutants. Two case studies are analysed in an attempt to identify a typical day in summer and in autumn.

[1]  H. Sebastian Seung,et al.  The Rectified Gaussian Distribution , 1997, NIPS.

[2]  Aapo Hyvärinen,et al.  Complexity Pursuit: Separating Interesting Components from Time Series , 2001, Neural Computation.

[3]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[4]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[5]  Ying Han,et al.  Structuring global responses of local filters using lateral connections , 2003, J. Exp. Theor. Artif. Intell..

[6]  Paul L. Rosin,et al.  Selection of the optimal parameter value for the Isomap algorithm , 2006, Pattern Recognit. Lett..

[7]  Michel Verleysen,et al.  Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis , 2004, Neurocomputing.

[8]  Colin Fyfe,et al.  Non-linear data structure extraction using simple hebbian networks , 1995, Biological Cybernetics.

[9]  Emilio Corchado,et al.  Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit , 2002, Data Mining and Knowledge Discovery.

[10]  E. Oja,et al.  Principal component analysis by homogeneous neural networks, Part I : The weighted subspace criterion , 1992 .

[11]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[12]  Colin Fyfe.  PCA Properties of Interneurons , 1993 .

[13]  Erkki Oja,et al.  Principal component analysis by homogeneous neural networks, part II: Analysis and extentions of the learning algorithm , 1992 .

[14]  Emilio Corchado,et al.  Connectionist Techniques For The Identification And Suppression Of Interfering Underlying Factors , 2003, Int. J. Pattern Recognit. Artif. Intell..

[15]  Pietro Perona,et al.  Grouping and dimensionality reduction by locally linear embedding , 2001, NIPS.

[16]  Dit-Yan Yeung,et al.  Robust locally linear embedding , 2006, Pattern Recognit..

[17]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[18]  Emilio Corchado,et al.  Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit , 2002, ICANN.

[19]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .