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.