Prediction of landslide displacement based on previous accumulated rainfall and Gaussian process regression model

Rainfall is an important factor inducing landslide deformation. Traditional displacement time series prediction models only consider historical displacement and do not consider the impact of rainfall to conduct landslide displacement prediction , resulting in significant errors in the medium to long term or long-term displacement prediction. Given that the Gaussian process regression(GPR) algorithm has the advantages of easy realization,adaptive acquisition of hyperparameter and probability significance of prediction output,a GPR prediction model of landslide displacement considering the previous accumulated rainfall by introducing the cumulative rainfall index is established in this study,which improves the long-term displacement prediction capability of the model. Taking the landslide in the southern mining area of Xichang as an example,the relationship curve of landslide displacement and daily cumulative rainfall was first analyzed,and the Pearson method was used to calculate the correlation coefficient between the landslide displacement and the previous cumulative rainfall days;Secondly,a GPR model was established,and trained and tested using existing monitoring data. The results showed that the prediction accuracy of the established model was significantly improved compared to the model without considering the previous accumulated rainfall. On this basis,a long-term displacement trend prediction was conducted for monitoring points S1–1 and S1–2,and a comparative analysis was performed on the displacement trend under the assumption of increasing rainfall by 10% and 20%. The results indicate that a 20% increase in rainfall leads to an increase in the deformation rate of the landslide to about 17 mm/d. Without taking control measures,the landslide will experience accelerated sliding to instability. © 2024 Academia Sinica. All rights reserved.