## 5 Conclusions  
This study analyzed the landslide susceptibility in the area surrounding the city of Cuenca (Ecuador) and used XGBoost for LSM generation because its results have been the best predictive capacity for the study area, according to the findings obtained previously. Several sets of factors were also used: the six best ones, obtained with feature selection in a previous stage: elevation, slope, TRI, distance to roads, lithology and NDVI; the DEM-dependent ones: aspect, curvature, elevation, slope, SPI, STI, TPI, TRI, TWI and solar radiation; and the non-DEM-dependent ones: land cover, distance to roads, distance to rivers, lithology and NDVI. In addition, two sampling methods were tested, the first based on the centroid of the landslides available in the applied inventory, and the second, by intensive point sampling, also based on the landslide polygons. To optimize the LSP model, hyperparameter tuning was performed based on seven important parameters of XGBoost and applying the trial-and-error method to find the ideal values of these parameters as well as to verify the computational performance of the model. The different LSMs showed different results, as each set of conditioning factors presented specific results, considering the aforementioned sampling methods. In this sense, it was determined that the six best factors obtained through feature selection applying the first sampling present the highest accuracy for the study area, according to the validation parameters and the level of coincidence of landslides in the high and very high susceptibility zones. This finding also confirmed the adequacy of the results obtained in the previous study of this research. On the contrary, and under the same conditions, the poorest result was obtained when applying the set of DEM-dependent factors with the first sampling. Thus, in general terms, the best adjustments are produced with the six selected factors, which integrate both DEM dependent and non-DEM dependent factors, indicating the need to use both sets; and the worst adjustments occur when only the DEM is used, which reinforces the importance of the non-DEM dependent factors, which allow modeling variables that have to do with the resistance of materials, soil support with vegetation cover and the influence of human actions. Sampling also influences the final result, with the former (by centroids) showing slightly better results than the latter (random), except in models obtained with no DEM-dependent factors, where random sampling better captures the discrete spatial variability of these variables. Although these results, together with those obtained in the previously elaborated studies, contribute to the scientific and preventive knowledge of landslides and allow defining crucial aspects to improve the accuracy of LSMs in the area, it is still necessary to implement additional models in future studies and to consider more conditioning factors, as well as to analyze other hyperparameter optimization algorithms, to improve the predictive capacity of the models. The research line generated with this study and its predecessors provides scientifically grounded inputs for the study of landslides in the proposed area, which has an evident lack of research applying the implemented methods and is prone to suffer the onslaught of these destructive events. These studies are expected to contribute to the management of natural risks in the area and generate a benefit for society in terms of prevention. Conceptualization, E.B.-L. and T.F. ; data selection and preparation, [PERSON]-L. and C.S. ; software implementation, [PERSON]-L.; methodology, [PERSON]-L., T.F. and C.S. ; validation E.B.-L. and T.F. ; investigation, E.B.-L.; writing--original draft preparation, E.B.-L.; writing--review and editing, [PERSON]-L., T.F., C.S. and [PERSON]-G.; supervision T.F. and [PERSON]-G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Universidad del Azuay (project No.