###### Abstract
The displacement back-analysis method is widely used in the parameter inversion of landslide masses and tunnel surrounding rock. [PERSON] and [PERSON] [9] developed a novel method for displacement back-analysis of rock slopes, using the Three Gorges Project site as a case study. With the development of machine learning, an increasing number of algorithms are being applied to data analysis in engineering fields, addressing many complex engineering problems. [PERSON] et al. [10; 11] employed 20 machine learning methods to assess the seismic response and performance of reinforced concrete buildings, and to analyze the seismic risk assessment of reinforced concrete structures. In recent years, combined with machine learning, the displacement back-analysis method has further developed. For example, scholars have used the neural network-genetic algorithm combination [12], artificial neural network optimization methods [13], and improved support vector machine methods [14; 15; 16] to conduct displacement back-analysis on the slope masses or tunnel surrounding rock, thereby determining the mechanical parameters of rock and soil materials. With the increasing construction of reservoirs, the study of seepage fields in reservoir dams and slopes remains a hot topic in geotechnical engineering. Many advances have been made in seepage back-analysis research, especially regarding the inversion of permeability coefficients of dam bodies or dam foundations [17; 18; 19; 20; 21; 22]. Some scholars also used machine learning methods to predict or inversely estimate the permeability coefficients of slope masses [23; 24]. [PERSON] et al. [25] conducted an inversion study of reservoir colluvial landslide permeability coefficient by combining physical models and data-driven models. [PERSON] et al. [26] proposed an evolutionary neural network inversion method to invert saturated permeability coefficient from the monitoring data. [PERSON] et al. [27] established a model integrating the relevance vector machine and cuckoo search to accurately determine the permeability coefficients of strata. However, most existing studies use steady-state analysis to estimate seepage parameters. For reservoir bank slopes, where GWL fluctuate with reservoir operations, steady-state analysis fails to capture the actual seepage parameters. Therefore, transient groundwater flow analysis should be used to invert the permeability coefficients. In the vast array of machine learning algorithms, support vector machine (SVM) is a regression method praised for its strong generalization and precise prediction. Compared to many neural networks, SVM is less likely to overfit with small training sets, thus havingan advantage in various scenarios. Furthermore, LSSVM extends SVM using a least-square approach to formulate the objective function, leading to more efficient solving processes and superior generalization capabilities. Therefore, LSSVM was selected as the primary machine learning approach in this paper for inversion analysis. This paper aims to obtain permeability coefficients of landslide mass suitable for transient analysis, with computed results consistent with monitoring data. To achieve this objective, two tasks were set: first, to construct training datasets for landslide permeability coefficients inversion based on finite element transient analysis; second, to compare the training effectiveness and inversion performance of LSSVM and its optimization algorithms to accurately determine permeability coefficients. The method proposed by this paper will provide new insights and reference to comprehensively determine the permeability coefficients of reservoir landslide.