Learning wind fields with multiple kernels

Loris Foresti*, Devis Tuia, Mikhail Kanevski, Alexei Pozdnoukhov

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)

Abstract

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Original languageEnglish
Pages (from-to)51-66
Number of pages16
JournalStochastic environmental research and risk assessment
Volume25
Issue number1
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Feature selection
  • Multiple kernel learning
  • Support vector regression
  • Topographic features/indices extraction
  • Wind resource estimation

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