基于RF-GWO的水利工程地质渗透系数智能反演分析

地质渗透系数是准确分析水利工程渗流的关键参数。针对传统反演方法计算效率低、精度差的问题，采用有限元正演模型和正交试验设计构建渗透系数反演样本集，建立了基于随机森林(RF)算法的渗流计算代理模型；在此基础上，引入灰狼优化(GWO)算法，提出了基于RF-GWO的渗透系数智能反演方法，并以Z抽水蓄能电站为研究案例进行了验证。结果表明：RF模型对各钻孔水位预测结果均接近实测值，性能优于CART和BP模型；GWO可搜寻到地质最佳渗透系数，钻孔水位反演结果合理，相对误差最大为0.42%，精度满足工程要求，计算的天然渗流场分布形态也符合一般山体渗流场分布规律。建立的反演模型能够快速准确地推断工程区地层渗透系数，具有实际工程应用价值。

Intelligent inversion analysis of geological permeability coefficients in hydraulic engineering based on RF-GWO

The geological permeability coefficients are the crucial parameters for accurately analyzing the seepage in hydraulic engineering. To address the issues of low efficiency and accuracy of conventional inversion approaches, this research utilized a combination of the finite element forward model and orthogonal experimental design to construct a sample set for the permeability coefficient inversion. Then, a permeability calculation surrogate model based on the random forest (RF) algorithm was developed. Subsequently, the grey wolf optimization (GWO) algorithm was introduced to develop an intelligent inversion method based on RF-GWO. Taking the Z pumped storage power station as the case study, it is found that the water level prediction outcomes of the RF model are closely aligned with the actual measured values at each borehole, with the performance surpassing CART and BP models. The optimal geological permeability coefficient calculated by GWO performs excellently in the borehole water level inversion, with a maximum relative error of 0.42%, thereby satisfying the required accuracy for engineering applications. The calculated natural seepage field distribution conforms to the general distribution patterns of mountainous seepage fields. Therefore, the proposed inversion model can rapidly and precisely predict the geological permeability coefficient in the project area, demonstrating significant engineering practicality and value.