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为解决分布式参数非点源污染(IMPULSE)模型不确定性分析中采样量和计算量过大的问题,在Bayes概率理论基础上,构建了基于Sobol序列的GLUE算法,用来描述多种扰动因素共同作用下的全局参数不确定性,从而对模型预测能力进行全面评价。将该方法应用于IMPULSE模型,对分布式参数的全局进行不确定性分析。结果表明:该模型结构优良,具有良好的预测能力,对空间不确定性有较高的预测稳定性和鲁棒性,可以满足实际流域污染模拟需要。
In order to solve the problem of oversampling and calculation in the uncertainty analysis of the distributed parameter non-point source pollution (IMPULSE) model, based on Bayes probability theory, a GLUE algorithm based on Sobol sequence is constructed to describe various disturbances The global parameter uncertainty under the influence of factors can make a comprehensive evaluation of the model predictive ability. The method is applied to the IMPULSE model to analyze the global uncertainty of distributed parameters. The results show that the model has good structure, good predictive ability and high predictive stability and robustness to space uncertainty, which can meet the needs of actual watershed pollution simulation.