## 1 Introduction  
Landslides are catastrophic events that occur mainly in mountainous areas and cause substantial losses of various kinds throughout the world [1, 2, 3]. These losses, evidenced in damage to infrastructure and human casualties, show the impact of these events. One way to counteract this impact is to generate resources that promote prevention and help mitigate their effects. To this end, the susceptibility to landslide occurrence should be analyzed through the generation of Landslide Susceptibility Maps (LSM). Susceptibility is the spatial probability of landslide occurrence, i.e., the possibility of a region being affected by this phenomenon based on certain characteristics (environmental, geological, geomorphological, among others) [4, 5]. Susceptibility makes it possible to know the areas with the highest probability of landslides [4; 6]; therefore, LSMs optimize landslide risk management and contribute to better territorial planning, since they show the probable distribution of landslides in a sector [7; 8]. These mapping resources are useful in places crossed or close to mountain ranges such as the Andes, where tragic events have occurred. Some of them are of recent occurrence as in Colombia: the cases of Mocoa in March 2017 ([PERSON] et al. [9]) and Choco in January 2024. While in Ecuador, Gulag-Marianza, near Cuenca in March 2022 ([PERSON] et al. [7]) and Alausi in March 2023 ([PERSON] et al. [10]), among others, were recorded. Due to the losses caused by these phenomena, it is necessary that competent institutions make the appropriate decisions and accurately determine the areas susceptible to these phenomena [11]. Previous research [7; 12] mentions the importance of Machine Learning (ML), which is based on quantitative methods widely used in Landslide Susceptibility Prediction (LSP) due to its higher predictive accuracy compared to other methods (heuristic, statistical, etc.) [13; 14]. In addition, several ML methods have been applied in different study areas around the world, and due to their popularity, they have also been implemented in areas with no previous studies or little research. Specifically, in some regions of Ecuador and Colombia, recent LSP research has been developed with different ML methods such as: Artificial Neural Networks (ANN) [12; 15], Random Forests (RF) [7; 15], XGBoost [7] and Linear Discriminant Analysis (LDA) [15]. Furthermore, in Ecuador, there is research applying ML such as [PERSON] et al. [16], who improved landslide prediction with deep learning on the Aloag-Santo Domingo highway; or [PERSON] et al. [17], who analyzed landslide exposure in fast-growing cities such as Cuenca (Ecuador). Similarly, in Colombia, [PERSON] et al. [18] generated landslide models in a tropical mountain basin (La Liboriana, Antioquia) using ML and [PERSON] et al. [19] compared susceptibility models based on data (including ML) in the eastern cordillera of the Colombian Andes. The association between past landslides and conditioning factors in each area should be considered [5], since the susceptibility is different in each sector, mainly due to their intrinsic characteristics and the types of data used [20]. It is still of interest to the scientific community to implement ML models with the appropriate combination of different conditioning factors and their spatial distribution [5; 20; 21]. Determining the optimal conditioning factors for LSP is still a challenge, as no rule defines which ones are the most important, nor how many are sufficient for a specific analysis [5]. However, several studies have been conducted in which the importance of conditioning factors in various areas has been determined using feature selection algorithms. In the study area proposed for this research, [PERSON] et al. [7] elaborated an analysis of conditioning factors using ML, obtaining six main factors for this area, which are described in that study. This research is pioneering both in the study area and in Ecuador. Several investigations have combined conditioning factors according to a given criterion.