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.