## 9 Conclusions  
The present work demonstrates the application of the landslide hazard methodology at three different times in the sector known as Cortinas (Colombia) (2017, 2021, and 2023), making it feasible to carry out this research and address the recurrence issue, which is a limitation of current methodologies. This method could be applied and extended to other case studies around the world, both in academic research and in geotechnical consulting. In the modeling performed for the three years mentioned, a high-hazard area of 27% of the study area was identified in 2017. In 2021, when the failure occurred, this area increased to 80%, and in 2023, it decreased to 35%. The SGC hazard zoning models, the Factor of Safety by the Infinite Slope method in GIS, and the Monte Carlo simulation model were used to process the different data obtained during the exploration campaign for each epoch. These models enabled the comparative analysis of changes in the conditioning factors that determine stability in each scenario. The above approach is based on the concept of accumulated geotechnical deterioration (AGD), which was introduced at a regional scale of 1:25,000 in the first article of this research. It will also be part of the analysis for the detailed scale 1:2000, which is the subject of a third article. This second part of this research reiterated the importance and necessity of analyzing the variability, randomness, and interdependence not only of the triggering factors but also of the conditioning factors under the concept of AGD. In contrast to the 1:25,000 scale, where the factors to be analyzed for deterioration were changes in geomorphology and vegetation cover, for the evaluation of AGD at the 1:5000 scale, the relevant parameters define the safety factor. In contrast, in order to apply the model, three types of slopes of different ranges, typical of the morphometry of the studied slope, were analyzed. Consequently, it is concluded that each time triggering actions occur, the variables for calculating the S.F. either deteriorate or recover, making the prediction of when these events will occur very complex. However, if AGD is considered, a better approximation can be achieved as long as periodic measurements, automatic simulations using AI, and the construction of big data--none of which are part of this stage of the research--are carried out. In terms of stability, the condition of the terrain should not be visualized as a photograph (as is customary) but as a sequence of photos (like a video). Although conventional prediction methods can identify areas of high danger, moderate-risk areas may eventually deteriorate, increasing their risk and requiring intervention. This approach, based on the novel AGD concept and validated through geophysical anomalies and real-time monitoring reports, proposes a proactive model. Conceptualization, C.A.B.B., A.M.M.-G. and M.Y. ; methodology, C.A.B.B., A.M.M.-G. and M.Y. ; validation, A.M.M.-G. and M.Y. ; formal analysis, C.A.B.B. ; investigation, C.A.B.B. ; resources, C.A.B.B. ; data curation, C.A.B.B. ; writing--original draft preparation, C.A.B.B. ; writing--review and editing, A.M.M.-G. and M.Y. ; visualization, C.A.B.B. ; supervision, A.M.M.-G. and M.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** The data that support the findings of this study are available upon reasonable request from the authors. **Acknowledgments:** The authors would like to thank the company E.D Ingeotencia S.A.S for providing the data and financial resources for this research. They would also like to thank the gas transportation company PROMIORIENTE SA ESP for its constant willingness to allow the research to be carried out for the preparation of this article. The GEAPAGE (Environmental Geomorphology and Geological Heritage) research group of the University of Salamanca assisted in this research.