The systematic approach of Chaotic in noise reduction to improve the accuracy of monthly Nahadchai River

Complexity and the dynamic behavior of nonlinear hydrological processes such as river flow that being necessary using of the mathematical models, intelligent, and new theories. The extent of the influence of noise on the analysis of hydrological (or any real) data is difficult to understand due to the lack of knowledge on the level and nature of the noise. Meanwhile, a variety of nonlinear noise reduction methods have been developed and applied to hydrological (and other real) data. Recent studies have shown that the noise limits the performance of many techniques used for identification and prediction of deterministic systems. The present study addresses some of the potential problems in applying such methods to chaotic hydrological (or any real) data, and discusses the usefulness of estimating the noise level prior to noise reduction. In this study, the model predictions with artificial neural networks has been studied for the monthly values of river flow in Nahandchai what demonstrating on the raw data and in noise-reduced data. The results indicate that acceptable accuracy estimates for the noise-reduced data

کلیدواژه ها:

River flow prediction ، chaotic theories ، artificial neural network ، Noise reduction ، Nahandchai