Prediction of River Water Turbidity Based on EMD-SVM

Due to the nonlinear and nonstationary characteristics of river water turbidity,a novel intelligent forecasting method based on empirical mode decomposition(EMD)and support vector machines(SVMs),is proposed.The intrinsic mode functions(IMFs)are adaptively extracted via EMD from a time series of turbidity according to the intrinsic characteristic time scales.Then tendencies of these IMFs are forecasted with SVMs respectively,in which the kernel functions are appropriately chosen with these different fluctuations of IMFs.Finally these forecasting results are combined to output the ultimate forecasting result.The proposed model is applied to a water turbidity tendency forecasting example,and the simulation results show that the forecasting performance of the hybrid model outperforms SVMs and RBF ahead forecasting.