Therefore, there are still considerable challenges in predicting slope stability. Moreover, it generally takes a long time to establish a computer model and perform analysis for numerical simulation methods (Abdalla et al. For the numerical simulation method, the selection of constitutive models, mechanical parameters and boundary conditions significantly affect its accuracy, and it often requires rich engineering experience and on-site back analysis to make a rational selection and acquire reasonable results (Qi and Tang 2018 Ray et al. For the limit equilibrium method, it is very difficult to find the critical slip surface because there are many potential slip surfaces (Yang et al. Both methods have their advantages and disadvantages. Many methods were proposed for a slope stability calculation, among which the limit equilibrium method and numerical simulation method were the two most widely used method (Yang et al. Their influence weights on slope stability vary, and there are complex nonlinear relationships between these control factors (Kang et al. Most of these control factors have the characteristics of randomness, ambiguity, variability and other uncertainties. Due to the complexity of the structure and discontinuity of the physical and mechanical properties of slope mass, as well as the variability and effectiveness of control factors acting on the slope, slope engineering can be regarded as an uncertain, nonlinear, dynamic and open complex system in which the stability is comprehensively affected by geological and engineering factors (Bui et al. The occurrence of landslides is a long-term process, which is nonlinear and complex from a gradual evolution to sudden macroscopic slope slippage (Kumar and Basudhar 2018 Criss et al. Therefore, it is very important to predict slope stability quickly and accurately. Accurately predicting landslides allows for promptly transferring personnel and property and minimizing losses (Huang et al. The mechanical balance of slopes is often destroyed due to natural or man-made factors, which induces landslides and other geological disasters and further causes casualties and economic losses (Palenzuela Baena et al. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models. The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. 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). As a result, a total of 2160 training samples and 450 testing samples are constructed. Each slope stability coefficient and its corresponding control factors is a slope sample. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. 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.

Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. 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. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. These issues can affect the accuracy of slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. Recently, machine learning models are commonly used in the slope stability prediction. The numerical simulation and slope stability prediction are the focus of slope disaster research.

Random forest vs neural network