Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models

Abstract

The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models.