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Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5769))

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Abstract

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

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Author information

Authors and Affiliations

  1. Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland

    Loris Foresti, Devis Tuia & Mikhail Kanevski

  2. National Centre for Geocomputation, National University of Ireland, Maynooth, Ireland

    Alexei Pozdnoukhov

Authors
  1. Loris Foresti
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  2. Devis Tuia
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  3. Alexei Pozdnoukhov
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  4. Mikhail Kanevski
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Foresti, L., Tuia, D., Pozdnoukhov, A., Kanevski, M. (2009). Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_94

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  • DOI: https://doi.org/10.1007/978-3-642-04277-5_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04276-8

  • Online ISBN: 978-3-642-04277-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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