An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features

ABSTRACT

We develop an integrated neural network landslide susceptibility assessment (LSA) method that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) deformation data and the spatial features of landslide influencing factors. We construct a time-distributed convolutional neural network (TD-CNN) and bidirectional gated recurrent unit (Bi-GRU) to better understand the temporal dynamic features of InSAR cumulative deformation, and construct a multi-scale convolutional neural network (MSCNN) to determine the spatial features of landslide influencing factors, and construct a parallel unified deep learning network model to fuse these temporal and spatial features for LSA. Compared with the traditional MSCNN method, the accuracy of the proposed model is improved by 1.20%. The performance of the proposed model is preferable to MSCNN. The area under the receiver operating characteristic curve (AUC) of the testing set reaches 0.91. Our LSA results show that the proposed model clearly depicts areas with very high susceptibility landslides. Further, only 10.18% of the study area accurately covers 84.79% of historical landslide areas. Subjective consequences and objective indicators show that the proposed model that is integrated time-series InSAR deformation dynamic features can make full use of landslide characteristics and effectively improve the reliability of LSA.

KEYWORDS:

• Landslide susceptibility assessment
• InSAR; time distributed convolutional neural network; bidirectional gated recurrent unit; remote sensing

Acknowledgments

We would like to express our great appreciation to the editors and three anonymous reviewers for constructive comments that helped improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement (DAS)

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 42201459; 42261076], Science and Technology Project of Gansu Province [grant number 23JRRA881] and Science and Technology Research and Development Plan of China State Railway Group Co., Ltd. (grant number P2021G047).