Predicting runoff with pre-processing approaches in Ardabil plain

Streamflow forecasting is required in many activities associated with the planning and operation of water reservoir systems. In hydrology, short and long-term streamflow forecasting are essential to optimize hydrological systems, which can lead to the expansion or reduction of future projects. In this research, using wavelet-based de-noising data and wavelet transform, a temporal pre-processing approach was applied to the monthly runoff time series in Samian station at the outlet of Ardabil plain. In the second stage, the rainfall-runoff modeling was performed by Artificial Neuron Network (ANN) for three different combinations of data. The first and second combinations of input were used from Samian station data but the third combination was used from the upstream runoff data of Samian station (Gilandeh and Kozatopraghi stations). The results showed that the serves of both wavelet-based de-noising data and wavelet transform (WT) techniques could improve the performance of the ANN rainfall-runoff modeling of the Samian station up to 4% and 18.5% in the verification phase. The third combination of data demonstrated better accuracy in comparison to the other data set.