Slope deformation prediction of SSA-SVR model based on GNSS monitoring
Slope deformation prediction is an effective method to study slope stability and early warning.There exists non-stationarity of high slope GNSS monitoring data and the existing noise affects the safety analysis of the slope.We take the ultra-deep cutting slope of Wuhua Highway as a case and propose slope deformation prediction model based on sparrow search algorithm optimized for support vector regression with smooth prior analysis decomposition and singular value decomposition denoising(SPA-SVD-SSA-SVR model).The influence of two data processing methods,namely decomposition and denoising,on the predic-tion results are compared.The results show that the high slope is in a safe state with overall small deforma-tion.The SSA-SVR model demonstrates improved prediction performance.Compared to the traditional SVR model,it reduces mean squared error(MSE)and mean absolute error(MAE)by 8.68％and 3.82％,respectively,for monitoring point G1,and by 11.60％and 3.26％,respectively,for monitoring point G2.Both SPA decomposition and SVD denoising can reduce the non-stationarity and noise impact of GNSS mo-nitoring data on prediction accuracy.However,the prediction accuracy of the single decomposition process is higher than that of the single denoising process.The integrated SPA-SVD-SSA-SVR model,which com-bines decomposition and denoising,shows better prediction performance.It reduces MSE and MAE by 31.06％and 19.59％,respectively,for monitoring point G1,and by 28.59％and 15.03％,respectively,for monitoring point G2.The research results provide new insights into the processing of slope deformation monitoring data and slope safety deformation prediction.
国家科技期刊平台
NSTL
万方数据
