| 桂春雷,石建省,刘继朝,马荣.含水层渗透系数预测及不确定性分析耦合模型[J].水利学报,2014,45(5): |
| 含水层渗透系数预测及不确定性分析耦合模型 |
| A coupling model for aquifer hydraulic conductivity prediction and its uncertainty analysis |
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| DOI： |
| 中文关键词: 渗透系数 ANN技术 贝叶斯方法 GLUE MCMC 不确定性 |
| 英文关键词: hydraulic conductivity ANN Bayesian GLUE MCMC uncertainty |
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| 中文摘要: |
| 本研究旨在精细计算冲洪积平原地区的渗透系数，并为进一步建立溶质运移模型提供基础数据。通过建立人工神经网络（ANN）与通用似然不确定估计法（GLUE）的耦合模型对含水层渗透系数进行预测，并对模型参数的不确定性进行分析。利用马尔可夫蒙特卡洛采样法（MCMC）取代常见的通用似然不确定性估计方法中的蒙特卡洛法（MC），将其与人工神经网络技术耦合，以150 个典型粒度组分样本作为输入数据，构建研究区含水层渗透系数预测及不确定性分析的GLUE-ANN 模型。通过对华北平原典型地区实例研究，验证该方法具有较好的采样效率和寻优性能。计算结果表明，与渗透系数的实测值相比较，GLUE-ANN 模型的相对误差介于1.55 %~23.53 %之间，模型的计算精度满足地下水资源评价的要求。通过模型参数的后验分布得出参数全局最优值所在的区域，表明模型能够更合理地反映水文地质参数的不确定性。 |
| 英文摘要: |
| This study aims at fine calculation of alluvial-proluvial plain region and providing fundamental data for construction of solute transport model in the further research. Hydraulic conductivities of aquifers in the study area are predicted through establishing a coupling model between artificial neural network （ANN） and generalized likelihood uncertainty estimation （GLUE）， and uncertainty of the model parameters is analyzed. Markov Chain Monte Carlo （MCMC） was used to replace Monte Carlo （MC） in common GLUE， and coupled it with artificial neural network technology， an overall model of aquifer hydraulic conductivity prediction and its uncertainty analysis （GLUE-ANN） was built by using 150 typical grain-size fraction samples as input data. Via case study in a typical area of North China Plain the study corroborates a better sampling efficiency and optimization capability; compared to measured values of hydraulic conductivity， relative errors of the GLUE-ANN model are between 1.55 % and 23.53 %， the calculation precision of the model meets the requirements of groundwater resources assessment. By posterior distributions of the model parameters， the areas of parameter global optimum are obtained， which indicates the model is capable of reasonably reflecting parameter uncertainty of hydrogeological model. |
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