Improving the Estimation of Simulated River Discharge Values Using State Space Structural Models

System simulation is done with different structures and by using different approaches and algorithms. Algorithms are intelligent methods of data processing in machine learning that can identify unknown factors in a time-dependent phenomenon. In the analysis of random phenomena, among the methods that can make decision-making easier is the ensemble algorithms. With the help of this method, more accurate data management and more knowledge of the studied system is obtained. Since, investigation of the trend component can be effective in simulating hydrological phenomena and help in interpreting the relationship between hydrological processes and environmental changes in the study areas; State space models have the advantage of analyzing the system flexibly and dynamically. Therefore, this article aims to improve the efficiency of Kalman Filter, ETS, BATS, and TBATS state space time series models with the help of an ensemble method and by comparing with the Box-Jenkins model, to show which of these models has a better capability in simulating the monthly discharge of the river. This comparison has been done in three water measuring stations of Sepiddasht Cesar, TangPanj Bakhtiari and Telezang in Dez catchments located in Khuzestan province since 1386 to 1399. The results of this study, based on the model evaluation criteria (RMSE, MAE and R2), showed that the state space performed better than the Box-Jenkins model (classical), and among the state space models, the local level model (Kalman filter) performed better. So that in the validation stage, RMSE = 39.21and R2 = 0.79 in Sepiddasht Cesar water measuring station, RMSE = 57.89 and R2 = 0.76 in TangPanj Bakhtiari station and RMSE = 113.41 and R2= 0.73 in Telezang station were obtained.