## 6 Conclusions  
Based on the multi-field monitoring data collected, clustering and data mining algorithms were employed to derive association rules among reservoir water levels, rainfall, and landslide deformation. Through a comprehensive analysis of the monitoring data, we reached the following conclusions:  
1. The clustering analysis and data mining algorithms utilized in this study effectively extracted association rules from a substantial volume of monitoring data, thereby revealing potential relationships among reservoir water levels, rainfall, and landslide deformation. The reliability of these findings has been thoroughly validated, and the methodologies can be applied to analyze the deformation characteristics and triggering factors of reservoir-induced landslides based on monitoring data. By identifying key correlations between triggering factors and landslide deformation, this approach is expected to support real-time monitoring systems, enabling the timely detection of risk factors for potential landslides. 2. The results of the association rule analysis indicate that reservoir water levels exert a relatively uniform influence on overall landslide behavior. Specifically, declines in reservoir water levels (\(-18.70\) to \(-2.16\) m/month) under medium-to-low levels (146.43 to 163.23 m) can significantly accelerate landslide deformation, whereas high reservoir water levels (165.37 to 175.10 m) and rising water levels (4.45 to 17.33 m/month) tend to suppress it. Rainfall appears to have a negligible effect on the front of the landslide but positively influences deformation in the middle and rear sections. Additionally, the high-level deformation phase at the rear section (3.5 to 6.3 m) is strongly consistent with that at the middle (5.0 to 7.9 m), but it differs from the trend at the front (6.0 to 21.0 m), highlighting the spatial variability in the deformation mechanisms. 3. The deformation velocity trends at monitoring points P5 and P7 are generally consistent, while P2 exhibits less consistency with these points. We hypothesize that this discrepancy is attributable to the proximity of the groundwater table to the front monitoring point and its greater distance from the middle and rear sections. The downslope-directed seepage pressure \(P_{w}\) significantly affects the front, resulting in an additional peak in velocity at low reservoir water levels following a decline. 4. Landslide deformation demonstrates a gradual increase from the rear to the front and from west to east, indicating that the reservoir water level near the front of the landslide plays a crucial role in accelerating landslide deformation. The movement pattern is in accordance with the behavior of a retrogressive landslide, supporting the classification of the No. 1 riverside sliding mass as a retrogressive type. This characteristic provides valuable insights for managing similar landslides that exhibit comparable geological and hydrological conditions, suggesting that priority should be given to drainage systems, such as drainage ditches and geo-drains, and support structures, like retaining walls and soil nails, at the landslide front to mitigate retrogressive-type deformation. In addition, these findings are expected to advance research on landslide deformation forecasting and the development of early warning systems. Conceptualization, Z.L., J.W. and G.C. ; Methodology, Z.L. ; Formal analysis, Z.L. ; Investigation, Y.L. ; Resources, G.C. ; Data curation, Y.L. ; Writing--original draft, Z.L. ; Writing--review & editing, J.W. ; Visualization, Z.L. ; Supervision, J.W. ; Project administration, G.C. ; Funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript. The work was funded by the Department of Natural Resources of Hubei Province, China (Grant No. ZRZY2024 KJ03). The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.