A time series based dynamic prediction model for loess mudstone landslides.

This study targets the poor dynamic adaptability of traditional prediction models for loess mudstone landslides, their neglect of time‑series patterns, and weak noise handling. Using the Zhangjiawan landslide as a case study, we propose a dynamic prediction model for loess mudstone landslides based on time‑series analysis. The model employs VMD to adaptively decompose the GNSS‑monitored cumulative displacement sequence into three physically meaningful components—trend, periodic and random. This decomposition reveals complex features caused by geological structures, rainfall, freeze–thaw cycles, and other factors. We design special prediction strategies for each component: the trend component is predicted using a sliding‑window improved quadratic least‑squares method that removes early data influence and improves responsiveness to deformation trends; the periodic component is further decomposed with the CEEMDAN–K‑means–VMD method and then predicted using an SSA‑Transformer‑BiLSTM model with rainfall and freeze–thaw factors; the random component is tested for white noise, and non‑white sequences are predicted using an SSR‑LSTM model based on historical deformation data. Applications show that VMD reduces data complexity, and the combined predictions agree well with measured values (MAE = 1.98, MAPE = 0.88%, R² = 0.93). The model improves the prediction accuracy and applicability for loess mudstone landslides and provides a reference for dynamic forecasting.