The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an ap...The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. “Ideal data” is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.展开更多
高校是国家创新体系的重要组成部分,为深入分析新形势下高校科技创新效率,完善高校科技创新效率评价方法,综合运用数据包络分析(data envelopment analysis,DEA)模型和基于信息熵法的理想点模型(technique for order preference by simi...高校是国家创新体系的重要组成部分,为深入分析新形势下高校科技创新效率,完善高校科技创新效率评价方法,综合运用数据包络分析(data envelopment analysis,DEA)模型和基于信息熵法的理想点模型(technique for order preference by similarity to an ideal solution model,TOPSIS),以天津市区域14所高校为例,对比分析了新形势下高校科技创新效率。基于DEA模型的分析结果表明,14所高校科技创新效率总体呈不规律波动趋势,即使继续加大投入力度,也仅能获得科技成果产出规模有限的增加;基于TOPSIS模型的分析结果表明,14所高校科技创新效率总体呈上升发展趋势,可继续加大投入力度,以促进科技成果更大规模地产出。通过两个模型的综合运用,实现了评价结果的交叉验证,为新形势下高校科技创新效率评价提供了新思路。展开更多
文摘The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. “Ideal data” is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.
文摘高校是国家创新体系的重要组成部分,为深入分析新形势下高校科技创新效率,完善高校科技创新效率评价方法,综合运用数据包络分析(data envelopment analysis,DEA)模型和基于信息熵法的理想点模型(technique for order preference by similarity to an ideal solution model,TOPSIS),以天津市区域14所高校为例,对比分析了新形势下高校科技创新效率。基于DEA模型的分析结果表明,14所高校科技创新效率总体呈不规律波动趋势,即使继续加大投入力度,也仅能获得科技成果产出规模有限的增加;基于TOPSIS模型的分析结果表明,14所高校科技创新效率总体呈上升发展趋势,可继续加大投入力度,以促进科技成果更大规模地产出。通过两个模型的综合运用,实现了评价结果的交叉验证,为新形势下高校科技创新效率评价提供了新思路。
