Chaotic feature analysis and forecasting of Liujiang River runoff

Abstract

Because most of runoff time series with limited amount of data reveal inherently nonlinear and stochastic characteristics and tend to show chaotic behavior, strategies based on chaotic analysis are popular methods to analyze them from real systems in nonlinear dynamics. Only one kind of predicted method for yearly rainfall-runoff forecasting cannot achieve perfect performance. Thus, a mixture strategy denoted by WT-PSR-GA-NN, which is composed of wavelet transform (WT), phase space reconstruction (PSR), neural network (NN) and genetic algorithm (GA), is presented in this paper. In the WT-PSR-GA-NN framework, the process to deal with time series gathered from Liujiang River runoff data is given as follows: (1) the runoff time series was first decomposed into low-frequency and high-frequency sub-series by wavelet transformation; (2) the two sub-series were separately and independently reconstructed into phase spaces; (3) the transformed time series in the reconstructed phase spaces were modeled by neural network, which is trained by genetic algorithm to avoid trapping into local minima; (4) the predicted results in low-frequency parts were combined with the ones of high-frequency parts, and reconstructed with wavelet inverse transformation, to form the future behavior of the runoff. Experiments show that WT-PSR-GA-NN is effective and its forecasting results are high in accuracy not only for the short-term yearly hydrological time series but also for the long-term one. The comparison results revealed that the overall forecasting performance of WT-PSR-GA-NN proposed by us is superior to other popularity methods for all the test cases. We can conclude that WT-PSR-GA-NN can not only increase the forecasted accuracy, but also its own competitiveness in efficiency, effectiveness and robustness.