Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS).

Landslides are one of the most significant natural hazards worldwide. They can have far-reaching negative impacts on societies in different socio-economic sectors as well as on the landscape. Among the different types and processes that can also affect infrastructure and land use planning, slow-moving landslides are often underestimated. Therefore, studying areas affected by slow movements provide an opportunity to better understand the spatial and temporal patterns of these processes, their forcings, mechanisms, and potential risks. This study aims to investigate the importance of historical data for improving landslide hazard assessment in Lower Austria (Austria), which is particularly prone to landslides. This paper focuses on how historical information formed the basis for the establishment of three long-term landslide monitoring observatories in this region. The analysis conducted highlights the importance of using historical data to better assess the frequency and magnitude relationships and phases of landslide activity. In particular, they can extend the temporal window and provide relevant information on past events and accelerations to improve knowledge of landslide dynamics and the resulting socio-economic impacts. In order to better assess the landslide hazard associated, it is necessary to integrate historical data and monitoring datasets obtained by surface and subsurface methods. Both components allow for the characterization of the spatio-temporal evolution of slow movements and the analysis of the hazard over time. Based on a variety of historical sources, it was possible to install the instruments constituting the long-term landslide monitoring observatories in a meaningful manner. The results demonstrate the influential role of human impact on the stability conditions, which may also contribute to landslide occurrence. In this regard, the attempt to combine historical data and long-term, continuous monitoring systems in the presented landslide observatories can improve landslide risk reduction measures in the region. The integration of different techniques and tools, along with ongoing research and collaboration with local authorities, will further improve our understanding of these slow-moving processes and the development of effective management strategies.

Monitoring surface displacements of landslides is essential for evaluating their evolution and the effectiveness of mitigation works. Traditional methods like robotic total stations (RTSs) and GNSS provide high-accuracy measurements but are limited to discrete points, potentially missing the broader landslide’s behavior. On the contrary, laser scanner surveys offer accurate 3D representations of slopes and the possibility of inferring their movements, but they are often limited to infrequent, high-cost surveys. Monitoring techniques based on ground-based digital photogrammetry may represent a new, robust, and cost-effective alternative. This study demonstrates the use of multi-temporal images from fixed and calibrated cameras to achieve the 3D reconstruction of landslide displacements. The method presented offers the important benefit of obtaining spatially dense displacement data across the entire camera view and quasi-continuous temporal measurement. This paper outlines the framework for this prototyping technique, along with a description of the necessary hardware and procedural steps. Furthermore, strengths and weaknesses are discussed based on the activities carried out in a landslide case study in northeastern Italy. The results from the photo-monitoring are reported, discussed, and compared with traditional topographical data, validating the reliability of this new approach in monitoring the time evolution of surface displacements across the entire landslide area.

Landslides are one of the most important and frequent geological hazards worldwide. Among the many different types and processes, slow and very slow mass movements are often underestimated, even if they can impact local infrastructures and permanently affect agricultural practices and land use planning. Slow-moving landslides are common in clay-rich layers and areas that are typically characterized by mechanically weak materials. In the field of slow-moving landslide monitoring, understanding the factors driving the slope instability is the key to assessing the landslide hazard and to supporting local authorities in hazard management.In this study, the first results of an ongoing monitoring setup for a complex, slow-moving earth-slide system in Lower Austria are presented. The Brandstatt landslide is located in a complex geological transition zone between the Flysch and Klippen zones, which is known to be prone to shallow and deep landslides because its susceptibility to sliding processes has been investigated in recent years. Considering the predisposing conditions (geological and climatic settings), the unstable slope can be considered as a representative site of complex landslide processes in this region.Landslide movements monitoring includes a combination of surface and subsurface methods to investigate the spatio-temporal evolution of factors that prepare, trigger, and control landslide dynamics. Geological characterization of the subsurface was obtained through a dynamic penetration test (DPH) campaign and percussion drilling. In addition, the subsurface displacements and potential shear planes were evaluated using repeated inclinometric measurements. A meteorological station is also installed on-site, as well as piezometers and time-domain reflectometry (TDR) sensors in selected locations on the slope. These instruments provide high temporal resolution data, which are automatically transmitted to a server for the real-time monitoring of hydrometeorological conditions. However, the monitoring strategy to detect surficial changes is currently limited to the application of Terrestrial Laser Scanning (TLS) because an Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) is not possible for vegetation cover issues.The current results suggest the following: i) the connection between soil properties, soil moisture, and changes in groundwater level in the evolution of the slope instability, ii) potential shear surfaces within the shallow layers of the unstable slope, and iii) the importance of combining hydrological and geotechnical monitoring to set up an integrated network for landslide interpretation. Accordingly, obtaining information from a multi-parameter monitoring system is fundamental to identifying the relationship between the triggering and kinematic mechanisms of a complex, slow-moving landslide. However, the nonlinear behavior of slow movements restricts the temporal capability to properly understand the processes of complex mass movements. Consequently, landslide dynamics need to be further understood to establish a long-term monitoring system.

This study presents a first assessment of the performances of a low-cost permanent laser scanning (PLS) system for geomorphic monitoring applications. The goal is to evaluate the applicability and accuracy of these accessible technologies in comparison with high-end, commercial laser scanning systems. The assessment focuses on accuracy estimations and reliability in detecting and quantifying geomorphic changes over time in a target area in the Rotmoos valley, located in the Ötztal (Tyrol, Austria), featuring sediment movement and riverbed changes that are manually induced in an experimental setup. In this study, we use a Livox Avia scanner, controllable via an open SDK and Raspberry Pi, as low-cost monitoring setup in comparison to a high-end RIEGL VZ-2000i TLS. We acquired 14 epochs of point clouds from both systems simultaneously while inducing changes to the scene in-between acquisitions. Changes are quantified via direct point cloud comparison using the M3C2 algorithm and assessed both spatially per epoch as well as regarding the time series information at selected locations. Our results show consistent change values and patterns obtained with both Livox and RIEGL scans, demonstrating that, despite minor differences in time series trends, the low-cost Livox scanner effectively captures geomorphic changes comparable to those measured by the RIEGL. Our presented approach, by leveraging affordable hardware and open-source software tools, could provide a cost-effective solution for long-term environmental monitoring. By comparing the results obtained from both systems, this research highlights the potential of low-cost alternatives for continuous geomorphic monitoring, offering valuable insights for cost-effective environmental management and research.