Research of spatial data interpolation algorithm based on SVR optimization by GA

Abstract

Traditional spatial interpolation method of spatial interpolation such as geometric method and function method cannot estimate the theoretical error and cannot appreciate the interpolation precision, but statistical methods require data to meet a certain spatial distribution. This paper describes the basic principle of SVR and applies SVR theory to spatial interpolation, combines the SVR with the genetic algorithm for the selection of hyper parameters of SVR has a direct impact on the forecast performance, proposes a new spatial data interpolation algorithm based on SVR optimization by GA. The algorithm uses genetic algorithm to achieve the optimization of the hyper parameters using for SVR. In the process of evolution, this paper uses five folds cross-validation as the fitness function to achieve the training model's best generalization ability. Spatial interpolation comparison 97 standard data sets are selected as research object. Experimental results show that, spatial data interpolation algorithm based on SVR optimization by GA are more accurate, and it has less dependence on the data itself and higher prediction accuracy, which is a new interpolation method with good prospect. © 2010 Springer-Verlag.