| Citation: | WANG Keqiang，LI Ming，LI Lianglong，et al. Research on displacement prediction model of colluvial landslides in Qinghai Province based on multiple influencing factors[J]. Bulletin of Geological Science and Technology，2026，45（4）：1-18 doi: 10.19509/j.cnki.dzkq.tb20250339
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Landslides, as one of the most prevalent geological hazards in China, are widely distributed and have also extended into the western regions. The Qinghai region is characterized by complex geomorphic units and clustered mountain systems, which provide favorable geological conditions for the initiation and development of landslides. A comprehensive investigation into the formation mechanisms and influencing factors of representative landslides in this region can provide essential theoretical support for landslide prevention, mitigation, and hazard forecasting, thereby reducing casualties and economic losses.
This study focused on the accumulation landslide group of Hanjiacun, Qutan Town, Ledu District, Qinghai Province. Based on field investigations and monitoring data, the macroscopic deformation characteristics and formation mechanisms of the landslide group were systematically analyzed. Furthermore, the correlation between rainfall, temperature, and the deformation time series was examined using wavelet coherence analysis. Temperature and rainfall were selected as the principal external variables. A linear regression ensemble model was employed, in which the predicted displacements from individual models were combined through a weighted summation approach to estimate the displacement at the GNSS2 monitoring point of the landslide.
The results indicated that the Hanjiacun landslide group exhibited an average annual deformation rate of approximately 8.5 mm, classifying it as a typical creep-type landslide. Its displacement demonstrated a step-like deformation pattern under the influence of both rainfall and temperature. Specifically, rainfall showed a positive correlation with cumulative displacement, with abrupt increases observed during periods of concentrated summer rainfall, followed by stabilization after the rainy season, while a lag effect was also evident. Temperature, in contrast, was negatively correlated with cumulative displacement. As temperatures decreased in winter, frost heave was induced by the freezing and volumetric expansion of pore water within the soil matrix of the slope, resulting in an increase in landslide deformation. With the onset of spring, thaw settlement caused a rebound phenomenon in the accumulation layer. The ensemble model achieved a goodness-of-fit of 0.990 for displacement prediction at the GNSS2 monitoring point and can accurately predict the landslide deformation of the landslide group.
The established multiple linear regression ensemble model shows excellent prediction accuracy and can be applied to short-term displacement forecasting of similar creep-type colluvial landslides in alpine cold regions.
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