Forecasting step-like landslide displacement through diverse monitoring frequencies

The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies. However, the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected. This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area (TGRA) as a case study. We collected surface automatic monitoring data at different intervals, ranging from daily to monthly. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency. Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The predictions from these models were integrated to derive cumulative displacement forecasts, enabling a comparative analysis of prediction accuracy across different monitoring frequencies. The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting, with optimal performance observed at moderate monitoring intervals. Intriguingly, the daily mean average error (MAE) decreases sharply with increasing monitoring frequency, reaching a plateau. These findings were corroborated by a parallel analysis of the Bazimen landslide, suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.