Streamflow Forecasting Using Wavelet- Gene Expression Programming Hybrid Approach and Assessing the Effects of Meteorological Parameters on its Capability

Forecasting stream flow is very important for planning and managing water resources systems and its precision has a significant role in accurately implementing the planned objectives. Besides, soft computing has a significant ability in modelling hydrologic processes. Therefore, in the present study, the Hybrid Wavelet-Gene Expression Programming model has been developed in comparison with its singular approach so that it forecasts the daily streamflow of Khoshkroud river located in Guilan province. For this purpose, in addition to the process of pre-processing hydrometric data, the effect of meteorological parameters on the model’s performance and efficiency has been studied. Also, pre-processing was performed with different properties and for four durations of one, two, three and six days. Correlation coefficient (R), index of agreement (Ig), Nash-Sutcliffe coefficient (NSE), mean absolute error (MAE), root-mean-square error (RMSE) and peak flow criteria(PFC) statistical indices were used to assess the models’ performances. The results show that using wavelet transform to pre-process hydrometric data will significantly improve the efficiency of the hybrid model in comparison with the singular model, such that the correlation coefficient of the validation data for three days has increased from 0.27 to 0.80 and similarly, the mean absolute error has decreased from 1.4 to 0.80 m3/Sec. On the other hand, meteorological parameters have caused the extreme values in the river’s flow rate time series to be well modelled and their efficiency in the extreme values to be significantly increased. The results obtained from this research express that the hybrid model alongside the meteorological parameters can be successfully and efficiently used in flow forecasting.