## 1 Introduction  
A landslide is defined as the movement of the slope covers, including soil, rock, and organic materials, under the influence of a gravitational force down the slope [1]. Among natural hazards (e.g., pollution, flooding, earthquakes, and landslides), landslides, the topic of this paper, rank seventh globally in terms of death and economic impact [2], including damage to roads rail lines, power lines, and touristic and historical [3; 4; 5]. Landslides can significantly affect the geomorphic evolution of the landscape that create some geological disasters throughout the world [6]. Iran is one such country; nearly 4900 destructive landslides recorded in the country up to the end of September 2007, causing approximately USD 12.7 billion (126,893 billion Iranian Rials) damage [7; 8]. Between 2500 and 4000, people have died in landslides between AD 763 and 2016 [9]. Slumps are the most common mass movements in the region [9]. Debris and rock avalanches are relatively uncommon but are responsible for much loss of life. Landslides are particularly frequent in the Alborz and Zagros Mountains [4; 9]. The risk posed by landslides in these areas is amplified by the inadequate scientific knowledge and resources aimed at dealing with the problem [5; 10]. The mountainous road of Salvat Abad saddle is one of the vital links of the strategic road network in Kurdistan province of Iran. This road corridor was severely affected by several landslides every year that caused damages to tens of thousands of dollars every year [11]. Considering the damage from the landslides particularly along the roads, monitoring the areas prone to landslide occurrence by preparing landslide susceptibility mapping is a mandatory for preventing and mitigating the most vulnerable areas [4; 12; 13]. According to the definition, landslide susceptibility refers to preventing and mitigating risk posed to the most vulnerable areas [14; 15; 16]. This map can specify the landslide prone areas with degrees of susceptibility so that land managers, governments, environmental planners, and policy makers can manage these areas before a catastrophic landslide. Indeed, the areas along the road, Salavat Abad, are classified into the same susceptibility classes to introduce these areas to implementing organizations, in order to manage them through control of the landslide before it occurs. Many methods have been developed for landslide susceptibility mapping, for example (1) expert knowledge-based models such as analytical hierarchy process (AHP) [17], PROMETHEE II, and fuzzy AHP [18]; (2) bivariate and multivariate statistical models such as frequency ratio (FR) [19; 20; 21]; index of entropy (IOE) [22; 23; 24], weighted linear combination (WLC) [25], certainty factors (CF) [26; 27], and logistic regression (LR) [15; 28]; (3) deterministic (physical-based) models such as Stability Index Mapping (SINMAP) [29], Shallow Landsliding Stability Model (SHALSTAB) [30], Self-Organized Slope (SOSlope) [31], PRobabilistIc MUltidimensional shallow Landslide Analysis (PRIMULA) [32], SHETRAN [33], Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model (TRIGRS) [34], and (4) machine learning models. Literatures show that AHP, LR, and SVM are the most commonly used methods [35]. Expert knowledge-based models are typically based on questionnaires and expert opinions that may differ from one expert to another and also may suffer from cognitive limitations centered around uncertainty and subjectivity [36]. Bivariate statistical methods, although robust and flexible, lack sensitivity in their analysis of conditioning factors and also oversimplify input data [37]. Multivariate methods allow users to order parametric importance before using modeling, but require a more profound knowledge of mathematics, statistics, and software [38]. Recently, machine learning methods have gained popularity over expert knowledge-based and bivariate and multivariate methods in studies of natural hazards because of their objectivity and high accuracy.