This paper presents a Least-Square Support Vector Machine (LS-SVM) approach for forecasting nonlinear hydrological time series. LS-SVM is a machine-learning algorithm firmly based on the statistical learning theory. The objective of this paper is to examine the feasibility of using LS-SVM in the forecasting of nonlinear hydrological time series by comparing it with a statistical method such as Multiple Linear Regression (MLR) and a heuristic method such as a Neural Network using Back-Propagation (NNBP). And the performance of prediction model is also dependent on the degrees of linearity (or persistency) of data, not only on the performance of model itself. Thus, we would clearly verify that prediction performance of three models according to linear extent using daily water demand and daily inflow of dam data. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to those of MLR and NNBP and LS-SVM demonstrated better forecasting efficiency in nonlinear hydrological time series using Relative Correlation Coefficient (RCC) which is a relative measure of forecasting efficiency with different persistency.