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通过建立云-Markov模型对含水层渗透系数进行预测。利用云模型中的多条件多规则不确定推理技术,根据样品的粒径分布对渗透系数进行预测,并对其进行误差分析;在此基础上利用权Markov原理对预测误差的随机性进行模拟,进而根据此模拟值对云模型的预测结果进行校正,将校正后的预测值作为云-Markov模型最终的计算结果输出,即完成对一个沉积样品渗透系数的预测。将该模型应用于华北平原典型地区冲洪积扇含水层参数研究,计算结果表明:与渗透系数的实测值相比,云模型的误差相对数介于0.62~1.34间,通过权Markov误差校正后,云-Markov模型的误差相对数为0.74~1.27。与传统经验公式相比,云和云-Markov模型的计算精度均满足地下水资源评价的要求,其中云-Markov模型具有更高的计算精度和广泛的使用范围,但其同时也具有较高的计算成本。
The permeability coefficient of aquifers is predicted by establishing cloud-Markov model. Based on the multi-conditions and multi-rules uncertain inference technology in cloud model, the permeability coefficient is predicted according to the particle size distribution of the sample and the error is analyzed. On the basis of this, Markov principle is used to simulate the randomness of prediction error, Then, the prediction results of the cloud model are corrected according to the simulation value, and the corrected prediction value is output as the final calculation result of the cloud-Markov model to complete the prediction of the permeability coefficient of a sedimentary sample. The model is applied to the alluvial fan aquifer parameters in the typical area of North China Plain. The calculation results show that the relative error of the cloud model is between 0.62 and 1.34, compared with the measured value of the permeability coefficient. After the weights Markov error correction , The relative error of cloud-Markov model is 0.74 ~ 1.27. Compared with the traditional empirical formula, the computational accuracy of cloud and cloud-Markov models all meet the requirements of groundwater resources evaluation. The cloud-Markov model has higher accuracy and wider range of applications, but it also has a higher calculation cost.