One Improved SAGA-ML Method for Parameters Estimation of Hydrologic Frequency Models

For solving the difficult problems of parameters estimation (influenced by model type, parameters number, complex computation process, etc) in the process of hydrologic frequency analysis by traditional methods, one improved method named SAGA-ML has been proposed as follows: firstly establishing one parameter optimization program, whose object function is the expression of minimal likelihood function about hydrologic frequency model, and the constraint equations are gotten by MOM, then SAGA is used to optimize parameters. This improved SAGA-ML method can overcome the disadvantages of conventional ML method because of using SAGA for parameter optimization. By Monte-Carlo tests, it has been concluded that the improved SAGA-ML method is effective and convenient when being used, and suitable for any hydrologic models, any numbers of parameters and any constraint conditions. So SAGA-ML becomes useful and effective both in theory and in practice.