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Slow-moving landslides have recently gained attention as geological hazards requiring long-term monitoring, as they can trigger large-scale slope failures or debris flows. Consequently, various studies have identified slow-moving landslides as precursors to large-scale landslides. However, conventional field instrumentation or GPS-based monitoring has limitations for long-term monitoring of large-scale areas. Consequently, SAR-based time-series displacement analysis is being utilized as an alternative. SAR time-series analysis offers the advantage of enabling long-term monitoring of ground displacement across extensive regions. Nevertheless, research on the interaction between the long-term displacement patterns of slow-moving landslides and their triggering factors remains insufficient. In particular, systematic research is needed on how the displacement observed over time interacts with static factors (topography, geology, etc.) or dynamic factors (precipitation, temperature). Existing statistical-based time series models are useful for clearly analyzing trends and seasonality in displacement data and understanding the underlying structure. However, they have limitations in fully reflecting nonlinear displacement patterns or complex interactions with various triggering factors.This study aims to perform time-series prediction using long-term SAR-based displacement data and analyze the relationship between displacement patterns and triggering factors from a data mining perspective. Specifically, it applies deep learning-based LSTM, capable of learning long-term dependencies, alongside existing statistical models for comparison and analysis. LSTM is evaluated as a model suitable for complex prediction of slow-moving landslides, as it considers the long-term cumulative effects of time-series data and can comprehensively learn nonlinear displacement patterns and multivariate data.Applying the method proposed in this study, the Gangwon Province area of South Korea was designated as the study region, and displacement data was constructed using Sentinel-1 SAR imagery acquired from 2014 to 2024. We examined the interactions between static and dynamic data expected to influence the constructed displacement data. We then performed long-term predictions using SAR-based displacement time series via deep learning-based LSTM to evaluate the potential for landslide monitoring from a long-term perspective. AcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2024-00358026 and RS-2025-00515970).