Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R² of 0.84), with FS decreasing with slope height.

In mountainous regions, where large valleys are essential corridors for settlements and infrastructures, landslide hazard management is a pressing challenge. Large, slow-moving landslides are sometimes difficult to detect. On the one hand, the identification of geomorphological evidence supported by a detailed analysis of possible geological predisposing factor is crucial. On the other hand, to confirm the state of activity of the landslide, displacements should also be detected through monitoring. However, monitoring is challenging when large areas and volumes are involved and when cost effectiveness is an issue. This study presents a comprehensive analysis of the Passo della Morte landslide system, located in the Carnian Alps, which has historically posed a significant threat to critical road infrastructures, including a 2200 m long tunnel. The area is exploited as an example of how an iterative 3M approach (Monitoring, Modeling, and Mitigation), can inform and update engineering geological models of unstable slopes by enabling a detailed comprehension of landslide dynamics, facilitating in turn the development of more effective strategies for risk management and mitigation. Through detailed investigation and continuous monitoring over nearly two decades, the engineering geological model has been refined, integrated with new field data, and has progressively improved understanding of slope instability processes. This work underscores the importance of a dynamic and adaptive approach to geological hazard management, providing a valuable framework for similar challenges in other regions.

Optical fiber technology has emerged as a remarkable tool for groundwater monitoring. It provides high-resolution spatial and temporal data alongside exceptional sensitivity. Increasingly applied to monitor groundwater parameters such as pressure, temperature, flow, and contamination levels, this technology provides a comprehensive framework for real-time hazard assessment and risk management by integrating geotechnical and environmental variables. This study focuses on the San Lorenzo tunnel, a site characterized by significant water inflows that pose substantial hydrogeological challenges. The primary objective is to monitor these inflows closely and develop effective risk mitigation strategies. Preliminary investigations have shown that the rock mass enclosing the tunnel is highly fractured, resulting in substantial water ingress, particularly during heavy rainfall. While water in the tunnel is well-documented, the precise localization and timing of these inflows remain inadequately understood. Distributed Temperature Sensing (DTS) was employed to address these gaps to monitor water inflows and temperature variations along approximately 700 meters of the tunnel. Temperature data were combined with conductivity measurements to infer the origin of the aquifers. Initial findings suggest the presence of at least two independent aquifers, potentially fed by a common upstream source. Three types of optical fibers were installed along the tunnel. The function of these fibers is to detect both the presence and temperature of inflows of water that are intercepted and channeled into conduits. This study aims to detect and assess the location of inflows along the tunnel, temporal and spatial evolution of the water accumulation and discharge associated with external meteorological events. By correlating these observations with environmental variables such as rainfall, snowmelt, groundwater levels, and the discharge of nearby springs, we seek to refine the hydrogeological conceptual model and evaluate the feasibility of extending the draining tunnel as a mitigation measure. This research demonstrates an innovative application of fiber optic technology to monitor subsurface water flow. It contributes to hydrogeological risk mitigation in the San Lorenzo tunnel and advances our understanding of groundwater dynamics in complex fractured rock environments.

This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering an enhanced capacity for monitoring faster-moving landslides. The methodology integrates multi-baseline interferometric analysis, utilizing backscattered signals from both point-like and distributed radar targets at full spatial resolution. The approach utilizes ten interferometric datasets acquired between 2017 and 2021 from both ascending and descending orbits. Each annual dataset is restricted to a six-month observation window to reduce temporal decorrelation effects. The HIA was implemented in a landslide-prone sector of the Dolomites, a UNESCO World Heritage Site located in the Eastern Italian Alps. Comparative evaluation against EGMS ground motion products demonstrates that the HIA significantly broadens the range of detectable slope instabilities, thus providing a valuable supplement to existing ground motion monitoring services and contributing meaningfully to landslide hazard assessment and risk reduction efforts.