Chaotic Bayesian optimal prediction method and its application in hydrological time series

AbstractThe embedding dimension and the number of nearest neighbors are very important parameters in the prediction of chaotic time series. To reduce the prediction errors and the uncertainties in the determination of the above parameters, a new chaos Bayesian optimal prediction method (CBOPM) is proposed by choosing optimal parameters in the local linear prediction method (LLPM) and improving the prediction accuracy with Bayesian theory. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set. The optimal parameters are selected by mean relative error (MRE) and correlation coefficient (CC) indices according to optimization criteria. Real hydrological time series are taken to examine the new method. The prediction results indicate that CBOPM can choose the optimal parameters adaptively in the prediction process. Compared with several LLPM models, the CBOPM has higher prediction accuracy in predicting hydrological time series

Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction

Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. A good problem decomposition method should provide enough diversity and also group interacting variables which are the synapses in the neural network. Neural networks have shown promising results in chaotic time series prediction. This work employs two problem decomposition methods for training Elman recurrent neural networks on chaotic time series problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the cooperative neuro-evolutionary methods. The results show improvement in performance in terms of accuracy when compared to some of the methods from literature

Modified frequency computation method for optimal environmental flows

The paper describes a modified frequency computation method to calculate the optimal environmental flows. This method was used to design monthly environmental flows in Lancang river. The environmental flows calculated by the method are compared with those by the ecological flow method and the Tennant method, revealing its effectiveness