Evaluation of surface deformation monitoring and prediction model performance in Banzi gully using combined time-series InSAR technology and CS-Elman neural network
The Banzi gully in Wenchuan County was significantly affected by the Ms8.0 earthquake on May 12,2008 resulting in the accumulation of loose materials in the gully.In recent years,landslide and debris flow disasters have occurred frequently.Addressing the complex geological structure of Banzi gully and the lack of effective monitoring and prediction of natural disasters,this study proposes an improved prediction model using a combination of InSAR technology and the Cuckoo Search algorithm to enhance the Elman neural network(CS-Elman)for surface deformation monitoring and prediction in the Banzi gully area.Firstly,SBAS-InSAR and PS-InSAR technologies were employed to process 22 scenes of C-band Sentinel-1 A data covering Banzi gully,obtaining surface deformation monitoring values.Then,by analyzing the correlation matrix,the optimal evaluation factors were determined from 12 factors including elevation,and a CS-Elman prediction model was constructed by combining the surface deformation monitoring values from a multi-factor perspective.Finally,the rationality and superiority of the CS-Elman model were analyzed through comparative experiments.Results indicate that:(1)SBAS-InSAR and PS-InSAR technologies show a high correlation coefficient(R2=0.91)between the monitored Line of Sight(LOS)deformation rates,proving the feasibility of combined analysis of the two techniques.(2)Selecting training sample sizes of 198,298,398 and 498 respectively,the maximum absolute errors between the predicted values of the CS-Elman model and the monitoring values of InSAR technology are 11.314 mm,6.188 mm,3.763 mm and 2.191 mm.The minimum values of Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percentage Error(MAPE)are obtained when the sample size is 498,which are 0.895 mm,1.712 mm,1.308 mm and 5.55％,respectively.(3)Randomly selecting 518 sample data points,the CS-Elman model yields MAE,MSE,RMSE and MAPE values of 1.206 mm,2.052 mm,1.432 mm and 6.09％,respectively,all of which are superior to those of the Elman model,verifying that the CS algorithm can effectively improve the prediction accuracy of the Elman model.(4)Through comparison with the GA-BP and CS-SVM models,the CS-Elman model demonstrates higher accuracy in surface deformation prediction,suggesting its effectiveness as a means for long-term deformation monitoring and prediction in Banzi gully.
Small Baseline Subset Interferometric Synthetic Aperture Radar(SBAS-InSAR)Persistent Scatterer Interferometric Synthetic Aperture Radar(PS-InSAR)Cuckoo search algorithmElman neural networkDeformation monitoringPredictive analysisDebris flowNatural c
NETL
NSTL
万方数据
