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- W2349582008 abstract "In this paper, an improved method for parameters estimation in hydrologic frequency analysis, SAGA-ML, was proposed. The idea of SAGA-ML is establishing one parameter optimization program whose object function is the minimal likelihood function of hydrologic probability distribution, and the constraint equations are gotten by MOM, then SAGA (Simulated Annealing Genetic Algorithm) is used to optimize parameters. The SAGA-ML method can overcome the disadvantages of normal maximum likelihood method because of using SAGA for parameter optimization. By Monte-Carlo tests, it has been concluded that the SAGA-ML method is effective and convenient when being used. The SAGA-ML method is suitable for any hydrologic probability distributions, any numbers of parameters and any constraint conditions, so it is useful and effective both in theory and practice." @default.
- W2349582008 created "2016-06-24" @default.
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- W2349582008 date "2009-01-01" @default.
- W2349582008 modified "2023-09-28" @default.
- W2349582008 title "Research on SAGA-ML Method for Parameter Optimization in Hydrologic Frequency Analysis" @default.
- W2349582008 hasPublicationYear "2009" @default.
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