TY - JOUR

T1 - A novel framework for landslide displacement prediction using MT-InSAR and machine learning techniques

AU - Zhou, Chao

AU - Cao, Ying

AU - Gan, Lulu

AU - Wang, Yue

AU - Motagh, Mahdi

AU - Roessner, Sigrid

AU - Hu, Xie

AU - Yin, Kunlong

N1 - Funding Information: This research is funded by the National Natural Science Foundation of China (No. 42371094 and No. 41702330 ) and Key Research and Development Program of Hubei Province (No. 2021BCA219 ). We would like to appreciate the editor and two referees for their comments which significantly improves this paper. The first author would like to thank the China Scholarship Council for funding his research at the German Research Centre for Geosciences .

PY - 2024/5

Y1 - 2024/5

N2 - The prediction of landslide deformation is an important part of landslide early warning systems. Displacement prediction based on geotechnical in-situ monitoring performs well, but its high costs and spatial limitations hinder frequent use within large areas. Here, we propose a novel physically-based and cost-effective landslide displacement prediction framework using the combination of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and machine learning techniques. We first extract displacement time series for the landslide from spaceborne Copernicus Sentinel-1A SAR imagery by MT-InSAR. Using wavelet transform, we then decompose the nonlinear displacement time series into trend terms, periodic terms, and noises. The advanced machine learning method of Gated Recurrent Units (GRU) is utilized to predict the trend and periodic displacements, respectively. The modeling inputs for trend and periodic displacement predictions are determined by analyzing their corresponding influencing factors. The total displacements are finally predicted by summing the predicted displacements of trend and periodic items. The Shuping and Muyubao landslides, identified as seepage-driven and buoyancy-driven, respectively, in the Three Gorges Reservoir area in China are selected as case studies to evaluate the performance of our methodology. The prediction results demonstrate that machine learning algorithms can accurately establish the nonlinear relationship between the landslide deformation and its triggers. GRU outperforms the algorithms of Long Short-Term Memory networks and Kernel-based Extreme Learning Machine, and the Adam algorithm can effectively optimize the model hyperparameters. The root mean square error and mean absolute percentage error are 3.817 and 0.022 in Shuping landslide, and 5.145 and 0.020 in Muyubao landslide, respectively. By integrating the advantages of MT-InSAR and machine learning techniques, our proposed prediction framework, considering the physics principles behind landslide deformation, can predict landslide displacement cost-effectively within large areas.

AB - The prediction of landslide deformation is an important part of landslide early warning systems. Displacement prediction based on geotechnical in-situ monitoring performs well, but its high costs and spatial limitations hinder frequent use within large areas. Here, we propose a novel physically-based and cost-effective landslide displacement prediction framework using the combination of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and machine learning techniques. We first extract displacement time series for the landslide from spaceborne Copernicus Sentinel-1A SAR imagery by MT-InSAR. Using wavelet transform, we then decompose the nonlinear displacement time series into trend terms, periodic terms, and noises. The advanced machine learning method of Gated Recurrent Units (GRU) is utilized to predict the trend and periodic displacements, respectively. The modeling inputs for trend and periodic displacement predictions are determined by analyzing their corresponding influencing factors. The total displacements are finally predicted by summing the predicted displacements of trend and periodic items. The Shuping and Muyubao landslides, identified as seepage-driven and buoyancy-driven, respectively, in the Three Gorges Reservoir area in China are selected as case studies to evaluate the performance of our methodology. The prediction results demonstrate that machine learning algorithms can accurately establish the nonlinear relationship between the landslide deformation and its triggers. GRU outperforms the algorithms of Long Short-Term Memory networks and Kernel-based Extreme Learning Machine, and the Adam algorithm can effectively optimize the model hyperparameters. The root mean square error and mean absolute percentage error are 3.817 and 0.022 in Shuping landslide, and 5.145 and 0.020 in Muyubao landslide, respectively. By integrating the advantages of MT-InSAR and machine learning techniques, our proposed prediction framework, considering the physics principles behind landslide deformation, can predict landslide displacement cost-effectively within large areas.

KW - Deformation characteristics

KW - Landslide displacement prediction

KW - Machine learning

KW - MT-InSAR

UR - http://www.scopus.com/inward/record.url?scp=85190495478&partnerID=8YFLogxK

U2 - 10.1016/j.enggeo.2024.107497

DO - 10.1016/j.enggeo.2024.107497

M3 - Article

AN - SCOPUS:85190495478

VL - 334

JO - Engineering geology

JF - Engineering geology

SN - 0013-7952

M1 - 107497

ER -