High slope deformation prediction based on residual modified ARIMA-GA-BP modeling

High slopes are susceptible to rainfall, weathering, earthquakes and human engineering construction, resulting in the originally stable high slopes are very prone to landslides and collapses, etc. In order to prevent such phenomena, in addition to optimizing the existing monitoring system, it is also crucial to establish a scientific and accurate prediction model for high slopes. In order to improve the prediction accuracy of high slope deformation data, it is proposed to establish a deformation data prediction model coupled with autoregressive integrated moving average model (ARIMA model), genetic algorithm (GA) and BP neural network. The model takes into account the linear and nonlinear parts of the data, and the ARIMA model is used for linear prediction, while the GA-BP algorithm regressively corrects the residuals of the ARIMA model prediction for nonlinear prediction. The results show that the residual-corrected ARIMA-GA-BP model has higher accuracy and extrapolation ability than other prediction models, and can be effectively applied in the research field of high slope deformation prediction.