# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 1 Introduction
Machine learning uses different applications of various algorithms, which are classified as shallow, and deep learning techniques also attempt to make predictions or decisions [31]. The predictions' accuracy directly depends on the learning rate of the algorithms, which can correspond to learning paradigms, such as supervised, unsupervised, or reinforcement learning [32]. No matter what type of learning is utilized in predictions, each algorithm can be analyzed for performance to understand its precision and accuracy [33]. The main ideas of using machine learning techniques to provide a safety factor or F.S for slopes can be classified based on several motivations: (i) F.S is a crucial value to the safety design, and an essential reliance of innovation and reliability against slope failure is directly related to this factor. Therefore, providing a reliable F.S with a low error rate and high precision is crucial. Traditional procedures are primarily based on simplified analyses and certain assumptions, which increase the error rate of calculations. (ii) In slope stability analysis, there are typically uncertainties in the evaluation process, which reduces the precision of calculations. Traditional methods usually do not have the possibility to cover such uncertainties. However, machine learning, with the amount of repetition in learning, can cover these uncertainties laterally and improve performance. (iii) The important point of reliance in machine learning analysis is the possibility of measuring the accuracy and performance of learning models during prediction processes, which can be estimated by the confusion matrix, error table, or loss function. There is no such capability in the usual stability analysis methods. Assessing the model's accuracy can be very useful in decision-making and conducting optimal stabilization strategies. Therefore, in traditional methods, stabilization strategies are made based on experience. Generally, machine learning techniques can effectively be used to develop robust predictive models for slope stability and to predict key parameters and stabilize behaviors in geotechnical engineering applications where uncertainty is inherent. Uncertainty considerations are the main deficiency in empirical and statistical assessments, leading to an increased error rate for stability analysis. The most significant uncertainties in estimating the slope safety coefficient (F.S) for various indicators include pore water pressure, geomaterial characteristics, slope geometry, and slope strength properties [4]. Therefore, it is considered a priority to use approaches that can best cover the uncertainties or have a better ability and performance than these uncertainties. Machine learning procedures are strong tools to reach that goal. The present study attempted to use several well-known machine learning algorithms to provide highly accurate predictive models to estimate the F.S for earth slopes. In this regard, the multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were considered as the primary analysis core. These algorithms have features and advantages that have caused researchers to use them in different works, especially geotechnical analysis and domain stability.