## 6 Conclusions  
In accordance with the above and considering the section on calculation methods for frequency ratio (FR), it can be concluded that establishing groupings or patterns of variation for continuous variables (the slope in this example) is worthwhile in all cases. As a result, the classes are better adjusted to the available data structure, and the subjectivity observed in many classifications of continuous factors is avoided. When performing an ROC analysis (see Figure 4), the continuous methods offer improvements in hits (positive and negative) in the sections of the ROC curve with the greatest susceptibility to landslides. According with [PERSON] (2022) (2020) (2020) (2020), in terms of model performance, the accuracy (by the AUC values) of the bivariate methods for landslide susceptibility mapping is approximately between 75-80% with study areas of some hundreds of square kilometers. The current investigation offers these AUC values, but with study areas ten times larger. An improvement of 2-3% on conventional methods is achieved. In these sections, continuous methods improve specificity. This is demonstrated by looking at the ROC curve and the pAUC curves, where the improvement is up to an FPR of 0.3-0.4 and a sensitivity (positive hit) of 0.8. According to the pAUC curves, the suitability of the RFR method is assured by its appearance as the outer envelope of the other methods. It is highly important that the regression fit method (RFR) is somewhat complex to apply but produces the best results. In the high susceptibility zone, the increase in sensitivity can be up to 10% over GFR for a given specificity. In addition, the different high susceptibility surfaces between RFR and GFR methods in the applied classification systems does not exceed 2% difference. Obviously, RFR offers a much more accurate high susceptibility surface. The GY-based classification system, which considers sensitivity and specificity, does reveal differences in FR methods. It improves the Rind index for VH and H levels and maintains the same efficiency by covering the same surface areas at these levels. It goes without saying that any improvement brought about by the application of new calculation methods to susceptibility maps represents an increase in their effectiveness and an advance in knowledge. The results obtained in a very large study area are sufficiently solid to recommend the general use of the new proposed methodologies under similar climates. In conclusion, the continuous methods presented in this paper for the calculation of the frequency ratio (RFR or even CFR) provide an improvement in specificity in areas of high susceptibility. In other words, areas without landslides are excluded with a higher probability of success (specificity), while maintaining a similar probability of success in locating these events (sensitivity). This is a key aspect that would avoid unnecessary costs in prevention. This improvement is transferred to susceptibility mapping much more clearly when classifications are used that incorporate sensitivity and especially, specificity parameters such as Generalised Youden. All authors contributed to conceptualization, led by [PERSON], who also conducted formal analysis and wrote the initial draft. [PERSON] had a leading role in GIS analysis. [PERSON] contributed to validation and data visualization. [PERSON] critically reviewed the paper and conducted the pre- and post-publication stage. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. The data processing scripts have been developed by the authors in Phyton 2.7 using the PyScripter IDE, for the acry geoprocessor of ESRI ArcGIS 10.7.