The generation of landslide present in the municipality of Capitanejo (Santander-Colombia) is conditioned to lithological, structural and morphometric factors that are related to the climatic activity. These factors describe a pattern of instability in the rocks, which generates falls, slides and flows. These conditions are seen in the results of a neural network model generated with the backpropagation algorithm. This model presented the conditions of the study area evaluated by the degree of susceptibility to landslide. The model predicted 92.86% of the data that were not entered in the learning module, which represents 50% of the landslides mapped in the region. This simulation generates different levels of susceptibility to landslide from very low to very high. In the study area, a tendency for moderate to very high susceptibility to landslides was found for the margins of the Chicamocha river valley. In turn, this area presents the largest number of active processes (falls and flows), which generates a problem that affects several levels within the sectors of production, communication and infrastructure of the community of Capitanejo.

The dynamic behavior of the basins evaluated by their morphometric parameters establishes a relationship with the endogenous and exogenous factors of the earth that control the modeling of the rocky massif by weathering and erosion processes. The characterization of these relationships can define the degree of affectation of the surfaces and the agents that control them as a categorical element in the definition of scenarios within the planning of physical and natural territory. This evaluation considers parameters contained within the characterization of the relief, shape and texture of the drainage and the mobility of the flow in the basin. As a result of this evaluation, three factors were obtained that control the processes in the basins: a tectonic structural factor followed by climate control and, finally, seismic activity that plays an important role in the mechanical weathering of surfaces. The correlation of these factors showed that the Umpalá, Guaca, Río Negro, Cantabara and La Cureña basins display a high degree of physical and mechanical weathering of the rocky massif and that the Manco, Talarcuta, Las Pavas, Felisco and El Abra basins are more likely to generate torrential flows. The joint evaluation of these parameters provides a useful tool to understand the dynamic behavior of basins and their impact on anthropogenic setting.

Mass movements are one of the hydrometeorological phenomena with the most negative impacts on the study area, and their evaluation through the calculation of susceptibility provides a tool of vital importance within territorial planning and disaster risk management on natural and anthropic environments. Their evaluation took algorithms designed within stochastic and statistical methods, such as the artificial neural network, the bivariate statistical method, and the logistic regression method, which integrate inherent variables (geoenvironmental characterization) against events or dependent variables. This correlation simulates regions with a probability of occurrence of mass movement under training or weight assignment. Its construction for this study took, as a basis, 50% of the events (test) and 50% of the events (validation) randomly and with equivalent area distribution against the inherent variables. As a result, it was observed that the bivariate method presented a good performance in spatial prediction. This model presents values of AUC = 82.2% (test) and AUC = 76.9% (validation), grouping a total of 591 events of the 856 events in the high category (69%). In turn, from a second evaluation carried out by this method to each hydrographic basin, a condition was established in the area (50 km²) for coherent results at a level of analysis 1:25,000, based on the idea that the variables do not present changes greater than 20% in their attributes, added to a knowledge of the area evaluated.

The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system.

The southeastern margin of the Ría de Arosa is a region of great ecological and social importance, characterized by increasing urban development, tourism pressures, and vulnerability to natural hazards, soil erosion, coastal flooding, and mass movements, where sustainable territorial planning poses significant challenges. This study combines Geographic Information Systems tools and quantitative and qualitative overlay techniques to integrate conservation quality and comprehensive risk maps. The main challenge addressed in this research is the integration of geospatial data and diverse natural risk factors. The result was a map of land use recommendations and limitations, and another of degree of land use limitation, which identify priority areas for conservation and zones suitable for the controlled development of recreational, agricultural, and industrial activities. The methodology employed allows for detailed modelling that is easily updated and applicable to other environments for territorial planning and natural resource conservation. Areas of special natural importance, such as Arosa Island and the El Grove Peninsula, stand out as optimal locations for sustainable recreational activities, while the northeastern coastal corridor, between Villanueva de Arosa and Cambados, shows suitability for anthropogenic development. This approach contributes to a balance between socioeconomic development and environmental protection, facilitating the implementation of sustainable planning and conservation strategies in highly fragile coastal areas.