Multi⁃step prediction method of landslide displacement based on fusion dynamic and static variables

In response to the problem that the landslide displacement prediction models are mainly based on one-step prediction combining dynamic variables such as rainfall and deformation，and lack of consideration of static variables such as time period related to multi-step displacement influence factors. A multi-step landslide displacement prediction model integrating dynamic and static variables was proposed. First，the variable selection network was used to select the initial input variables，excavate the highly correlated variables with the daily displacement of the landslide，and weaken the influence of redundant variables on the model. Then，the static variables were integrated into the network，and the dynamic correlation of time was adjusted by encoding the context. Finally，the multi-step displacement prediction of landslide was realized by capturing the long-term dependence of time series with multi-head attention module. Taking Xinpu landslide in Chongqing as an example，the method is compared with DeepAR and Long Short-Term Memory（LSTM）models. The experimental results show that the method can achieve more robust and high-precision multi-step displacement prediction of landslide. © 2023 Editorial Board of Jilin University. All rights reserved.