New applications for information fusion and soil moisture forecasting

There is much concurrent ongoing research to develop, advance and apply new techniques capable of addressing the diverse applications and complexities of data fusion. In this paper we demonstrate the success of statistical learning theory-based support vector machine (SVM) and sparse Bayesian learning-based relevance vector machine (RVM) to perform reliable predictions. The prognostic capability of SVM and RVM will be utilized to achieve high level inference. The plausibility of these techniques is shown by their superior performance in forecasting soil moisture providing exogenous knowledge.