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传统优化算法和现代优化算法均是基于目标函数曲面寻求优值,而大多数参数率定方法的目标函数均为误差平方和。直观看来,基于误差平方和的目标函数曲面往往较参数函数曲面更为复杂。此外,这些算法常常无法证明寻得的优值是全局优值,而且计算过程较复杂,效率较低。鉴于此,提出函数曲面参数率定方法,并应用于SAC理想模型和实际模型的参数率定。首先,通过理想模型验证了该方法的可行性;其次,通过东张流域13年日资料和13场洪水验证了该方法的实际应用效果。结果表明,该方法在实际应用中是可行的、效率与精度皆较高,是一种有效的参数优选方法。
Traditional optimization algorithms and modern optimization algorithms are based on the objective function surface to seek merit, while most of the parameters of the calibration method of the objective function is the sum of squared error. Intuitively, the objective function surface based on the squared error square is often more complicated than the parametric function surface. In addition, these algorithms often can not prove that the merit obtained is a global merit, and the calculation process is more complex and inefficient. In view of this, the method of parameter calibration of function surface is proposed and applied to the parameter calibration of SAC ideal model and actual model. First, the feasibility of this method is verified by the ideal model. Secondly, the practical application of this method is validated by 13 years of daily data and 13 floods in the Dongzhang watershed. The results show that this method is feasible in practice and has high efficiency and precision, which is an effective parameter optimization method.