Analysis of cross-correlated chaotic streamflows

A trial is made to explore the applicability of chaos analysis outside the commonly reported analysis of a single chaotic time series. Two cross-corre lated streamflows, the Little River and the Reed Creek, Virginia, USA, are analysed with regard to the chaotic behaviour. Segments of missing data are assumed in one of the time series and estimated using the other complete t ime series. Linear regression and artificial neural network models are empl oyed. Two experiments are conducted in the analysis: (a) fitting one global model and (b) fitting multiple local models. Each local model is in the di rect vicinity of the missing: data. A nonlinear noise reduction method is u sed to reduce the noise in both time series and the two experiments are rep eated. It is found that using multiple local models to estimate the missing data is superior to fitting one global model with regard to the mean squar ed error and the mean relative error of the estimated values. This result i s attributed to the chaotic behaviour of the streamflows under consideratio n.