###### Abstract  
The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage. p 2024 14 5822 122 1222 12222 12222 12222 122222 122222 122222 1222222 1222222 1222222 12222222 12222222 12222222 12222222 122222222 122222222 1222222  
The seepage field of landslides is crucial for studying the mechanism of reservoir landslide formation and deformation failure modes [1; 2; 3; 4]. Currently, the main methods for studying seepage fields in reservoir landslides include theoretical analysis, field monitoring, and numerical simulation. Among these, field monitoring of the GWL in landslides shows the dynamic response of GWL in a landslide mass to reservoir water levels (RWL), which helps analyze landslide seepage fields. However, this monitoring data alone cannot fully capture the periodic variations in the reservoir landslide seepage field and its impact on the landslide stability. Therefore, further research using theoretical analysis or numerical simulation is necessary. For both theoretical analysis and numerical simulation, determining the permeability coefficient of a landslide mass is essential. The difficulty in parameter determination has always been a bottleneck in theoretical analysis and numerical simulation in geotechnical mechanics. Traditional testing methods such as pumping tests [5; 6] are simple and convenient. However, because the test locations are discrete and the parameters have nonuniformity, the results often fail to reflect the comprehensive seepage parameters of rock and soil masses [7]. Due to practical constraints, it is also difficult to conduct relevant tests on deep-seated rock masses in landslides. In addition to experimental methods, permeability coefficients of aquifers can be determined using analytical methods [8], but this route is limited to simple seepage field conditions or simplified assumptions. The relationship between seepage parameters of landslide masses and GWL is complex and nonlinear, so it is difficult to directly express using formulas. With the development of monitoring technology, using monitoring data to invert the parameters of landslides has become an important approach over the past decades.