State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network

Abstract

Identifying the states of hydrodynamic pressure-driven landslides can more effectively assist in the analysis of the landslide deformation law, and accurately identifying the landslide state is of great significance for the in-depth study of the hydrodynamic pressure-driven landslide state. Aiming at the problem that there are few abrupt states of hydrodynamic pressure-driven landslides, it is difficult to obtain relevant features, which leads to poor state recognition performance, and a generative adversarial network learning method for landslide state recognition is proposed.In this method, the landslide state monitoring data matrix is constructed, a reasonable generator network is designed based on a small number of data samples to complete the data amplification of the landslide states, and the discriminator network is designed to realize the screening of the amplified data, and the classifying landslide states being realized through the confrontation generation network to achieve the purpose of landslide status identification. Taking the Baishuihe landslide in the Three Gorges Reservoir area as the research object, multisource monitoring data such as rainfall, reservoir water level, deep displacement, and surface displacement are normalized. The states recognition generative adversarial network completes the classification and identification of the landslide state. The results show that the generative adversarial network has high accuracy for landslide state recognition. The research method in this paper can accurately identify and classify the landslide state in the target area.

Keywords

• hydrodynamic pressure-driven landslide
• generative adversarial network
• landslide state
• baishuihe landslide