# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 1 Introduction  
The stability of local slopes is a critical issue that needs to be meticulously investigated due to their great impact on the adjacent engineering projects (excavation and transmission roads, for instance). In addition to that, slope failures cause great psychological damage (e.g., the loss of property and human life) all over the world every year. In Iran, for example, based on a rough estimation announced by the Iranian Landslide Working Party (2007), 187 people have been killed every year by slope failure in this country [1]. The saturation degree, as well as other intrinsic properties of the soil, significantly affect the likelihood of slope failure [2; 3]. Up to now, many scientists have intended to provide effective modeling for slope stability problems. Some drawbacks of traditional methods, such as the necessity of using laboratory equipment and the high level of complexity prevent them from being a simply used solution [4]. Moreover, they cannot be used as a comprehensive solution due to their limitation in investigating a specific slope condition (e.g., slope angle, height, groundwater level, soil properties, etc.). Different types of limit equilibrium methods (LEMs), finite element model (FEM), and numerical solutions have been extensively employed for the engineering problem [5; 6; 7; 8]. Development of design charts has been investigated for years in order to provide a reliable tool for slope stability assessment [9]. However, this approach is not devoid of defects either. Generating an efficient design chart requires consuming a lot of time and cost. Furthermore, determining the precise mechanical parameters is a difficult task [10; 11]. Hence, the application of artificial intelligence techniques is more highlighted due to their quick performance and sufficient accuracy. The prominent superiority of these approaches is that they are able to detect the non-linear relationship between the target parameter(s) and its key factors. Also, ANN can be implemented using any determined number of hidden nodes [12; 13]. For example, the suitable efficiency of an artificial neural network (ANN) [14; 15; 16] and a support vector machine (SVM) [17] has been proven in many geotechnical studies. After reviewing the literature, the complexity of the slope stability problem is evident. What makes the issue even more complicated and critical is constructing various structures in the vicinity of slopes (i.e., indicating a noticeable number of loads applied on a rigid footing). The amount of surcharge and the distance from the slope's crest are two parameters influencing the stability of the purposed slope [18]. This encouraged scholars to present an equation to calculate the factor of safety (FOS) of pure slopes or the slopes receiving a static load [19; 20; 21; 22; 23]. Prior to this study, [PERSON] and [PERSON] estimated the FOS for 200 slopes with different geometric and shear strength parameters using the multiple linear regression (MLR) and ANN models. They also compared the obtained results with an FEM model, and an acceptable rate of accuracy was obtained for both applied models. In this research, ANN outperformed MLR. Also, [PERSON] et al. [23] effectively used the random forest (RF) and regression tree predictive models in functional soil-landscape modeling for regionalizing the depth of the failure plane and soil bulk density. They developed a classification for detecting the safe and hazardous slopes by means of FOS calculations. In some cases, the random forest and logistic regression techniques were implemented for a susceptibility modeling of a shallow landslide phenomena. They found that applied models have an excellent capability for this work. The SVM model synthesized by a minimal sequential optimization (SMO) algorithm has been used for surface grinding quality [24] and many medical classifications [25].