Mine water discharge prediction based on least squares support vector machines

In order to realize the prediction of a chaotic time series of mine water discharge, an approach incorporating phase space reconstruction theory and statistical learning theory was studied. A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space. We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series. The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model. The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.