Improving the performance of the Hymod Model using the Whale optimization algorithm

Today, the use of hydrological models is mainly necessary to simulate changes in water source and flow (runoff and evaporation). Proper modeling of hydrological processes requires the determination of model parameters. In calibration processes, the values ​​of the model parameters are estimated so that the model can simulate a natural system well. It is generally impossible to estimate the parameters of such models directly due to the large number of parameters and it is necessary to estimate them with the help of optimization tools (model calibration). In the present study, the parameters of the daily Hymod rainfall-runoff model (a simple conceptual rainfall-runoff model) were calibrated using the Whale algorithm (WOA), which is derived from the way whale food is searched. The evaluation of the mentioned calibration method was performed using daily precipitation, evapotranspiration and transpiration data for 5 years and its validation was performed in 5 years in the Leaf River Basin of the United States. The simulated and observed flow rates were compared using correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliffe coefficient (NS). The values ​​of error measurement criteria were 0.91, 1.2 and 0.8 for the calibration period and 0.91, 2.5 and 0.83 for the validation period, respectively. Also, the parameters calculated using the whale algorithm, the maximum moisture storage in the area of 216.95 mm, spatial variation of soil moisture storage 0.38, the distribution factor between the two moisture tanks 0.98, the shelf life in the laminar tank 0.08 days and Shelf life in fast flow tank is 0.47 days. Examination of error values ​​showed that the Whale Optimization Algorithm has high efficiency in calibrating rainfall-runoff models.