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

Authors:
Loris Foresti;Devis Tuia;Alexei Pozdnoukhov;Mikhail Kanevski
Affiliations:
Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland;Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland;National Centre for Geocomputation, National University of Ireland, Maynooth, Ireland;Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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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.