Slope Deformation Prediction Based on MT-InSAR and Fbprophet for Deep Excavation Section of South–North Water Transfer Project

Abstract

In the operation and maintenance of the South–North Water Transfer Project, monitoring and predicting the canal slope deformation quickly and efficiently is one of the urgent problems to be solved. To predict the slope deformation of the deep excavated canal section at the head of the canal. We propose a new idea of adopting the joint prediction of MT-InSAR and Fbprophet. Firstly, MT-InSAR monitoring technology was used to invert channel deformation using 88 Sentinel-1A orbit-raising image data with a time baseline from 2017 to 2019. The time-series deformation of nine monitoring points was also extracted, and it was found that the time-series curves of the cumulative deformation of the channel slope showed fluctuations. The Fbprophet algorithm was then used to train the prediction model in Python to predict the channel slope deformation over the next 365 days. Finally, the prediction results were compared with the MT-InSAR monitoring values to analyze the prediction accuracy and applicability of the Fbprophet algorithm for the slope deformation monitoring of the South–North Water Transfer Project. The results show that: the deformation rate of the slope of the deep excavation section is in the range of 10 mm/a to 25 mm/a, the maximum accumulated deformation is about 60 mm, and the slope of the excavation canal shows a lifting phenomenon; among the nine monitoring points, the minimum and maximum predicted values of deformation using the machine learning prediction model trained in this paper were 56 mm and 73 mm, respectively; comparing the predicted and monitored values, their correlation coefficients were 0.998 at the highest and 0.988 at the lowest, and the minimum and maximum values of RMSE (RootMean Square Error) were 0.72 mm and 2.87 mm, respectively. It shows that the prediction model trained by the Fbprophet algorithm in this paper applies to the prediction of slope deformation in the deep excavation section, and our prediction results can provide a data reference for disaster prevention and the sustainable development of the South–North Water Transfer Project.

Spatiotemporal evolution of deformation and LSTM prediction model over the slope of the deep excavation section at the head of the South-North Water Transfer Middle Route Canal

“…4 . When interfering, filtering, and untangling the generated image pairs, the classical frequency domain rate filtering algorithm Goldstein method [ 33 , 34 ] was used for filtering and the Minimum Cost Flow (MCF) method [ 35 , 36 ] was chosen for untangling, and it was found that the best result was achieved when the threshold value of the untangling correlation coefficient was set to 0.35. The ground control points were obtained using the second detection method for orbit refinement, re-de-platforming, and two inversions.…”

Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model

“…The VMD-SSA-LSTM time prediction model combines the advantages of VMD, SSA, and LSTM techniques. It decomposes the original data using VMD, optimizes the LSTM model using SSA, and finally uses LSTM for prediction [27,30] . This combined model exhibits high prediction accuracy and flexibility in handling complex time series data.…”

“…[34] designed to handle time series data. It introduces gating mechanisms (such as forget gates, input gates, and output gates) to address the issues of gradient vanishing and gradient explosion in traditional RNNs [27,35] .…”

“…However, studies on large-scale cluster mining areas are relatively few. In this study, we take Jincheng City, China, as an example and use a novel method combining MT-InSAR [25,26] technology and LSTM [27,28] neural networks to monitor and analyze mining area subsidence. We aim to reveal the surface subsidence phenomenon in large-scale mining clusters.…”