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This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed.

The monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period.

Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.

Many landslides have been triggered by the fluctuation of reservoir water level in the Three Gorges Reservoir area (TGRA) of China. The current research lacks the quantitative risk assessment framework for reservoir landslides. In this study, the risk associated with the Shilongmen reservoir landslide in TGRA is assessed by considering two hazard scenarios corresponding to the deformation and failure phases. The former is modeled via fully coupled finite element analyses, which take into account the reservoir water level and rainfall as triggering factors of landslide displacements; the latter, via the limit equilibrium method and Monte Carlo method, analyzes the failure probability under different conditions induced by the annual reservoir regulation and the return period of rainfall events. The vulnerability is quantitatively modeled as a function of both landslide intensity and the resilience of the elements at risk (EAR). In the deformation scenario, the expected monetary loss is estimated by multiplying the monetary value of the buildings for their vulnerability, and the corresponding risk map displays the potential economic losses to the EAR. In the failure scenario, direct (landslide) and indirect (tsunamis) risks are considered because the rapid decline of reservoir water level and heavy rainfall can cause the landslide rapidly sliding into the Yangtze River. Bearing in mind that there are several unstable slopes in the same geo-environmental context in the TGRA, the proposed research framework could provide a reference for government risk management strategies.

The paper investigates the changes in the physical, mechanical, and hydraulic properties of coarse-grained pyroclastic soils, considered under both wildfire-burned and laboratory heating conditions. The soil samples were collected on Mount “Le Porche” in the municipality of Siano (Campania Region, Southern Italy), hit by wildfires on 20 September 2019. The area is prone to fast-moving landslides, as testified by the disastrous events of 5–6 May 1998. The experimental results show that the analyzed surficial samples exhibited (i) grain size distribution variations due to the disaggregation of gravelly and sandy particles (mostly of pumice nature), (ii) chromatic changes ranging from black to reddish, (iii) changes in specific gravity in low-severity fire-burned soil samples different from those exposed to laboratory heating treatments; (iv) progressive reductions of shear strength, associated with a decrease in the cohesive contribution offered by the soil-root systems and, for more severe burns, even in the soil friction angle, and (v) changes in soil-water retention capacity. Although the analyses deserve further deepening, the appropriate knowledge on these issues could provide key inputs for geotechnical analyses dealing with landslide susceptibility on fire-affected slopes in unsaturated conditions.

The paper presents a multi-source approach tailored for the analysis of ground movements affecting the village of Barberà de la Conca (Tarragona, Catalonia, Spain), where cracks on the ground and damage of different severity to structures and infrastructure was recorded. For this purpose, monitoring of ground displacements performed by topographic survey, geotechnical monitoring and remote sensing techniques (ground-based synthetic aperture radar, GBSAR) are combined with multi-temporal damage surveys and monitoring of cracks (crackmeters) to get an insight into the kinematics of the urban slope. The obtained results highlight the correspondence between the monitoring data and the effects on the exposed facilities induced by ground displacements, which seem to occur predominantly in the horizontal plane with diverging directions (northward and southward) from the main ground fracture crossing the centre of the village. The case study stands as a further contribution to fostering this kind of integrated approaches that via cross-validations can improve data reliability as well as enrich datasets for slope instability recognition and analysis, which are crucial to plan risk mitigation works.

Background In subtropical-typhoon regions, prolonged rainfall often triggers landslides, creating challenges in predicting soil thickness and its spatial distribution due to dense vegetation and complex topography. This study aims to tackle these issues by developing a watershed-scale map of unstable layer thickness to improve predictions of debris flows and landslides. Methods We integrated geomorphological surveys with ensemble machine learning techniques. Unmanned Aerial Vehicle technology was used to create a 3D digital elevation model, identifying different Quaternary deposits in the study area. Fieldwork involved three stages: soil thickness data collection via field surveys, geophysical exploration for spatial distribution, and core drilling for geotechnical properties. Separate evaluation systems were built for eluvium and slope deposits. A machine learning model was developed on a Python platform to predict soil thickness, using data from mountainous watersheds along China’s eastern coast. Results The model accurately predicted 84.7% of eluvium soil thickness (average 0.64 m, mainly sandy clay) and 81.3% of slope deposit thickness (average 2.34 m, including sandy clay and crushed stone). For eluvium, the root mean square error was 0.148 m, and for slope deposits, it was 0.27 m. Key influencing factors were lithology for eluvium and elevation for slope deposits. Shallow landslides were most prevalent in these layers, with sliding surfaces at specific interfaces between material types. Conclusions This study demonstrates the effectiveness of combining geomorphological surveys and machine learning for precise soil thickness prediction. The methodology enhances geohazard models, offering insights into landslide behavior and supporting more accurate risk assessments. These findings provide a foundation for future research on mitigation strategies in similar regions.

This paper proposes a three-phase method that combines multi-source (i.e. topographic, thematic, monitoring) input data in a GIS environment to rank—at small (1:250,000) scale—administrative units (e.g. municipalities) based on their exposure to slow-moving landslide risk within a selected area (e.g. a region) and, accordingly, detect those primarily requiring mitigation measures. The method is applied in the Calabria region (southern Italy) where several municipalities are widely affected by slow-moving landslides that systematically cause damage to buildings and infrastructure networks resulting in significant economic losses. The results obtained are validated based on the information gathered from previous studies carried out at large (municipal) scale. The work undertaken represents a first, fundamental step of a wider circular approach that can profitably facilitate the decision makers in addressing the issue of the slow-moving landslide risk mitigation in a sustainable way.

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.