Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

Landslides pose significant risks to towns and villages in Southern Italy, including the San Marco dei Cavoti hamlet (Benevento, Campania), where settlements have expanded into areas threatened by landslides, leading to property damage, disruption to the social fabric and loss of life. This study aims to investigate the surface deformations in the area using Differential Interferometry SAR (DInSAR) analysis on COSMO-SkyMed radar imagery and to assess the potential implications for landslide activity. The DInSAR analysis methodology allowed us to obtain high-precision results presented as time series diagrams and maps of cumulative displacement for the study area. Furthermore, the displacement rates derived from the DInSAR analysis were decomposed into vertical and horizontal components to provide better insights into the slope processes and their potential impacts on the San Marco dei Cavoti hamlet. Our significant findings revealed active slope movements and the uphill enlargement of previously inventoried landslides threatening the San Marco dei Cavoti hamlet. These insights contribute to a better understanding of the landslide dynamics in the region and highlight the areas that may require further investigation or intervention measures. In conclusion, this study demonstrates the effectiveness of DInSAR analysis in providing valuable insights into landslide dynamics and informing potential mitigation measures for at-risk communities. This technique could be applied to other landslide-prone regions to support informed decision-making and enhance the safety and resilience of affected communities.

Accurately characterizing the kinematics of slow-moving urban landslides remains a major scientific and operational challenge, because no single monitoring technique can simultaneously provide spatially continuous deformation patterns and reliable three-dimensional displacement measurements. This study investigates the spatial and temporal evolution of a slow-moving landslide affecting the University of Azuay campus in Cuenca (Ecuador), where ongoing ground deformation has caused structural damage to several buildings. An integrated monitoring strategy combining GNSS measurements, Sentinel-1 multi-temporal DInSAR analysis, and geophysical investigations (ERT and seismic profiling) was adopted to characterize landslide kinematics and constrain subsurface conditions. GNSS observations revealed that the north–south displacement component was dominant, with cumulative displacements exceeding 20 cm during the monitoring period (from July 2021 to June 2024), while east–west displacements were on the order of 10 cm. MT-DInSAR analysis delineated the spatial extent of the unstable area and identified mean deformation rates of up to approximately −1.5 cm/year in the central sector of the landslide. The combined interpretation of geodetic and geophysical data indicates that slope instability is controlled by saturated fine-grained layers and mechanical contrasts, with the basal sliding zone associated with weak levels of the Mangan Formation. Overall, the results demonstrate the value of a multi-sensor, component-wise monitoring strategy for improving the reliability of deformation estimates and for supporting landslide risk assessment and land-use planning in complex urban environments.

Geohazards represent a severe danger to human life and the entire socio-economic fabric all over the world and have gained a pivotal role in urban planning and infrastructure modernization. Using machine learning algorithms and ensemble modeling techniques, geohazards can be modeled and studied with accurate results. While this approach allows the characterization of geohazard impact on urban areas, the study of multirisk and the influence that geohazards assume on each other is still a challenge. Combining the statistical methods with the Rock Engineering Systems, an in-depth analysis of mutual interaction between geohazards can be produced on a multirisk analysis basis. The results describe the geohazard relationships through the Interaction matrix, based on the expert knowledge of the user, and a multirisk map, built from the geohazard susceptibility obtained from the statistical approach, that highlights areas that could be affected by “cascade” or combined effects.