A novel self-organizing neural technique for wind speed mapping

Abstract

Systems with high nonlinearities are, in general, very difficult to model. This is particularly true in geostatistics, where the problem of the estimation of a regionalized variable (RV) given only a small amount of measurement stations and a complex terrain surface is very challenging. This paper introduces a novel strategy, which couples the Curvilinear Component Analysis (CCA) and the Generalized Mapping Regressor (GMR). CCA, which is a nonlinear projector of a data manifold, is here used in order to find the intrinsic dimension of the data manifold, just giving an insight on the nonlinearities of the problem. This analysis drives the pre-processing of the data set used for the training phase of GMR. GMR is an incremental, self-organizing neural network which is able to model nonlinear functions by transforming the approximation into a pattern recognition problem. The presented approach is tested on the spatial estimation of the wind speed over the complex terrain of the isle of Sicily, in Italy. Wind speed maps resulting from this technique are presented and compared with a deterministic interpolator, the Inverse Distance Weighting (IDW) method.