Foresti , Loris and Tuia, Devis and Kanevski, Mikhail and Pozdnoukhov, Alexei
Learning wind fields with multiple kernels.
Stochastic Environmental Research and Risk Assessment, 25 (1).
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.
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