顾及InSAR形变的CNN滑坡易发性动态评估——以刘家峡水库区域为例

doi:10.13722/j.cnki.jrme.2022.0266

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Abstract The landslide factors used in the existing landslide susceptibility evaluation methods are mostly static data(eg. topography，geology) and lack of dynamic data(eg. surface deformation)，which cannot fully extract the characteristics of deformation landslides，resulting in poor reliability of landslide susceptibility evaluation. Synthetic aperture radar interferometry(InSAR) data can reflect the deformation characteristics of landslides in both vertical and horizontal directions. In this study，two-dimensional InSAR deformation data are introduced as dynamic factors，topography，geology，hydrology and humanities are combined，and a convolutional neural network(CNN) model is constructed for dynamic evaluation of landslide susceptibility. Multiple evaluation metrics are used to evaluate the model accuracy，while landslide susceptibility evaluation results with and without taking into two-dimensional InSAR factors are compared. The proposed method is compared with support vector machines(SVM) method，in addition，the proposed method is applied in different cases. The results show that the overall accuracy of multiple evaluation indexes considering InSAR deformation dynamic factors has been improved，and the model has shown good results in identifying landslide susceptibility areas that are in slow deformation，and the recognition rate has been improved from 0.78 to 0.93 in the training data. In the validation data，the recognition rate is improved by 0.21，revealing that considering the 2D InSAR deformation factors can improve the accuracy of dynamic evaluation of landslide susceptibility. The CNN model constructed in this study has more reliable evaluation results compared with SVM method，and is more applicable and generalizable in different cases. The high susceptibility areas in the Liujiaxia Reservoir are mainly located in the northeast of Yangta Township，Hongquan Township and Sanluo Township of the mountainous areas，real-time monitoring should be strengthened. The dynamic evaluation model of landslide susceptibility using CNN considering 2D InSAR deformation factors proposed can provide the new ideas for landslide disaster prevention.

Key words： slope engineering landslide susceptibility evaluation 2D time series InSAR deep learning CNN

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.0266 OR http://rockmech.whrsm.ac.cn/EN/Y2023/V42/I2/450

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