## 1 Introduction  
Landslide susceptibility maps (LSM) are valuable and necessary resources for deciding how land should be planned, particularly in mountainous areas. Different techniques and methods for determining susceptibility have been developed since the 1970s, including classic statistical, index-based, and machine learning methods [1]. GIS technology is a useful tool for managing and analysing spatial data in relation to landslides [2, 3]. Table 1 shows description advantages and disadvantages of the different techniques used. Historical landslide inventory data have been fundamental in the preparation of these maps. These data are generally used as contrasts for validation elements to adjust susceptibility values, and especially to establish the levels or classes of the variables that govern the ground-motion mechanisms. This is the case of the 'frequency ratio method' (FR). However, it is sometimes felt that these inventory data could be better exploited. In other words, they could provide a better fit in the case of continuous variables, as well as in the mapping classification of susceptibility values. The likelihood of landslides in a particular area has been widely evaluated using the frequency ratio (FR) method [6; 7]. This is a type of bivariate statistical analysis technique that allows for representing landslide susceptibility maps that link the individual factors governing landslide failure to the sensitivity of its occurrence. FR can be easily integrated into a GIS system due to the simplicity of its principles, making it user-friendly [8; 9]. However, other methods have proven to be more effective, which highlights the need to improve the FR technique [10; 11]. The initial step in using the FR method to analyse landslides (as well as other bivariate methods) is identifying and grouping the factors that contribute to them, such as the angle of the slope and the type of rock, among others. The likelihood of a landslide occurring in a specific area is determined by the numbers and threshold limits of the classes of factors. Determining the thresholds that lead to best results constitutes a major challenge, because it relies on expert knowledge of the area in question and is therefore subjective [12; 13; 14; 15]. One proposal is to use procedures capable of extracting the maximum information from the available data. This paper proposes a method that objectively considers the nature of the inventory data to determine the number of classes and their limits for numerical variables. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Description** & **Advantages** & **Disadvantages** \\ \hline \multicolumn{4}{p{113.8 pt}}{} \\ \multicolumn{4}{p{113. The commonly used factor classifications induce discontinuities in the FR values. This implies significant breaks between classes, which will eventually lead to a discontinuity in the spatial distribution of landslide susceptibility. Furthermore, the choice of factor class boundaries is subjective. Therefore, it is of key importance to apply new methods to avoid data gaps and subjective decisions. Creating clear and easy-to-understand maps is an important aspect of mapping landslide susceptibility. However, grouping data into a set number of classes can have a negative impact on the accuracy of the results, depending on how the classes are defined. Thus, changing the boundaries between classes can lead to vastly different maps [16]. Therefore, the overall accuracy of the classified map is crucial for determining its usefulness in decision-making tasks [17; 18]. To assess the reliability of a map for a particular application, it is necessary to make meaningful and consistent measurements of its accuracy. Currently, there are no standard guidelines for classifying landslide susceptibility maps, such as the number and names of classes or class boundaries.