## 7 Conclusions  
In this study, a method to inversely estimate the permeability coefficient of a landslide mass using GWL monitoring data was proposed. The Majiagou landslide in the Three Gorges Reservoir area was used as an example. The permeability coefficients of the landslide were obtained through field monitoring, finite element simulation, and machine learning. The verification results matched the field monitoring results. The following conclusions were drawn.  
1. The feasibility of using LSSVM for the inversion of landslide permeability coefficients has been well validated. Additionally, it has been confirmed that training data generated from finite element transient analysis of seepage can be used to accurately invert permeability coefficients.
2. LSSVM, PSO-LSSVM, and WOA-LSSVM were effective in fitting the nonlinear relationship between landslide permeability coefficient and GWL. Among them, the WOA-optimized LSSVM algorithm had the highest efficiency and fitting degree. However, during the inversion process, PSO-LSSVM had the smallest error and best inversion effect, whereas WOA-LSSVM had the largest error and a slightly poorer inversion effect. Therefore, multiple methods should be compared to select the best parameter inversion values.
3. The PSO-LSSVM inversion obtained the following permeability coefficients of the Majiagou landslide mass: gravelly soil k\({}_{1}\) = 4.385 \(\times\) 10\({}^{-2}\) cm/s; highly weathered fractured rock k\({}_{2}\) = 5.819 \(\times\) 10\({}^{-3}\) cm/s; mid-weathered rock k\({}_{3}\) = 6.284 \(\times\) 10\({}^{-4}\) cm/s. Inputting these values into the model to simulate GWL yielded simulated values that closely matched the measured values, thus indicating the high reliability of the inversion results and strong feasibility of the method. Additionally, this method could be extended to the inversion analysis of deformation parameters and strength parameters of a landslide mass.  
This study will facilitate subsequent fluid-solid coupling analyses of landslides, offering a foundation for a deeper understanding of the mechanisms driving landslide evolution. Thus, it will provide a theoretical basis for accurately predicting and effectively preventing reservoir landslides.  
**Author Contributions:** Conceptualization, X.T.; methodology, X.T. and C.S.; software, X.T. and Y.Z.; validation, X.T. and C.S.; formal analysis, X.T.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, X.T.; writing--review and editing, C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 52379100 and 41831278).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The water level data of the Three Gorges Reservoir can be accessed from the website of Three Gorges Navigation Authority. The website link is [[https://sxth.mot.gov.cn/sj/](https://sxth.mot.gov.cn/sj/)]([https://sxth.mot.gov.cn/sj/](https://sxth.mot.gov.cn/sj/)), accessed on 1 May 2024. The MATLAB codes for study are available upon reasonable request to the corresponding author.  
**Acknowledgments:** The authors would like to thank the research group of [PERSON] at China University of Geosciences, for their contributions to the field experiments on the Majiagou landslide, as well as for providing data and assistance for this study.  
**Conflicts of Interest:** The authors declare that they have no competing interests.