Estimation of hydraulic conductivity of landslides based on support vector machine method optimized with genetic algorithm

Estimation of hydraulic conductivities (K) of the rock media in a landslide is the basis for the study of the seepage field and multi-dimensional evolution of the reservoir bank slope. Traditionally, in-situ tests and indoor tests are used to determine the hydraulic conductivity of landslide rock and soil, but this method is costly and the test location has a certain randomness. In this study, the Majiagou landslide in the Three Gorges Reservoir area is taken as an example, and a method for inverting the K values of the deformed rock and soil mass using the groundwater level dynamic monitoring data is proposed. The basic idea is as follows. First, build a numerical model of the landslide based on the landslide survey data and water level observation data. Afterwards, SPSS is used to generate different orthogonal test combinations of hydraulic conductivity, substitute the hydraulic conductivity into the numerical model to calculate the water levels of the monitoring wells, and obtain the data of hydraulic conductivity and corresponding simulated water levels. Finally, the support vector machine (SVM) optimized with the genetic algorithm (GA) is used to construct a nonlinear mapping relationship between slope water level and hydraulic conductivities (K). The results obtained are then replaced for the monitored water levels to obtain the hydraulic conductivities of the landslide rock and soil which is used to develop the finite element model. The model is then verified by comparing the simulated water levels with the observed water levels. The inversion of the Majiagou landslide hydraulic conductivity shows that the SVM optimized with GA yields a good agreement between the simulated and real data and has a very efficient and accurate search results. The inversion accuracy of K based on the GA-SVM method meets the needs of practical applications.