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参数识别是水文模型应用的前提,在参数识别的同时需要充分考虑模型自身及参数的不确定性。引入了一种具有较强全局与局部搜索能力的多目标优化算法(MOCOM—UA),探讨了该算法在多目标基础上融合遗传算法和单纯形法两类算法不同搜索机制的模型优化方案,并以汉江上游江口流域新安江模型降水—径流模拟实践为例,将MOCOM—UA算法应用于新安江模型的参数识别,得到了4目标函数情形下的Pareto参数空间和模型的预测范围,并根据该算法模拟计算的结果,初步分析了参数和模型的不确定性。
Parameter identification is the prerequisite for the application of hydrological model. At the same time, the parameter identification needs to take full account of the uncertainty of the model itself and the parameters. A multi-objective optimization algorithm (MOCOM-UA) with strong global and local search capabilities is introduced. The model optimization scheme of different search mechanisms based on genetic algorithm and simplex method based on multi-objective is discussed. Taking the Xinanjiang model precipitation-runoff simulation in Jiangkou basin as an example, the MOCOM-UA algorithm is applied to the parameter identification of Xin’anjiang model. The Pareto parameter space and the prediction range of the model under the 4-objective objective function are obtained. The algorithm simulates the calculation results and analyzes the uncertainty of the parameters and models.