Streamflow prediction using a new approach of hybrid artificial neural network with discrete wavelet transform. A case study: the catchment of Seybouse in northeastern Algeria

Y. Tikhamarine, D. Souag-Gamane, S. Mellak

Abstract


Abstract: Accurate streamflow values prediction is vitally important for hydrology and hydrogeology in water resources management system.  Daily and monthly streamflow prediction can help in water management domain, regulation distribution of dams and estimation of groundwater level, especially in drought and flood issues. It contributes to improve long and short-term time series by using previous information, therefore a power performance model should be used to process the complex nonlinear relation between the predictor and predictive variables.

This present study investigates the performance of hybrid artificial neural network (ANN) with discrete wavelet transforms (DWT) and compared with the single model of artificial neural network (ANN) based on feed forward Back-propagation technique and Bayesian regularization algorithm. The monthly streamflow data from the Bouchegouf gauge station on Seybouse watershed (Code 14.05.01) in Algeria River is used in this study. The statistical evaluation performance criteria used are: root mean square error (RMSE), mean absolute error (MAE), Nash Sutcliffe efficiency (NSE), and correlation coefficient (R) were employed to evaluate the results performances.

The obtained results indicate that conjunction of discrete wavelet transform with artificial neural network performed better than the single (ANN) and this hybrid model could be a useful tool for solving many prediction issues.


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