PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DINSAR TECHNIQUE

Abstract. Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points

Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing

Potential of remote sensing data to support the seismic safety assessment of reinforced concrete buildings affected by slow-moving landslides

Different forms of hazard can affect structures throughout their existence. The occurrence of a seismic event in areas exposed to different risks or already affected by other phenomena is highly likely, especially in countries characterized by high seismicity and equally high hydrogeological risk, as Italy. Nevertheless, the seismic safety assessment of reinforced concrete (RC) structures is commonly carried out considering the seismic action only, generally applied to an analytical model, neglecting the stress–strain state induced by previous ongoing phenomena. The aim of this work is to highlight the importance of the seismic safety assessment in a multi-hazard analysis, cumulating the action coming from two different hazards: landslide and earthquake. An existing RC building, located in an area affected by an intermittent landslide phenomenon with slow kinematics, that may also be subjected to strong earthquakes, is used as case study. The Differential Synthetic Aperture Radar Interferometry (DInSAR) approach is used to monitor the evolution in time of the landslide. DInSAR deformation data are used to detect surface ground movements applied to building foundations. A non-linear static analysis procedure is implemented for the code-based seismic safety assessment, in two different scenarios. The seismic assessment of the case-study building is implemented in a condition of structure deformed only for gravity loads, and, then, in a state of known landslide-induced deformed configuration. A comparison is proposed between the building seismic safety assessment performed in both cases, with or without the consideration of the landslide-induced displacements, showing the importance of a multi-hazard evaluation

Landslide hazard and land management in high-density urban areas of Campania region, Italy

Abstract. Results deriving from a research focused on the interplay between landslides and urban development are presented here, with reference to two densely populated settings located in the Campania region, Italy: the city of Naples and the island of Ischia. Both areas suffer adverse consequences from various types of landslides since at least 2000 yr. Our study evidences that, despite the long history of slope instabilities, the urban evolution, often illegal, disregarded the high landslide propensity of the hillsides; thus, unsafe lands have been occupied, even in recent years, when proper and strict rules have been enacted to downgrade the landslide risk. It is finally argued that future guidelines should not be entirely based upon physical countermeasures against mass movements. On the contrary, national and local authorities should enforce the territorial control, obliging citizens to respect the existing regulations and emphasizing the role of alternative, non-structural solutions

SAR data and field surveys combination to update rainfall-induced shallow landslide inventory

The Campania region has been recurrently hit by severe landslides in volcanoclastic deposits. The city of Naples, and in particular the Camaldoli and Agnano hills (Phlegraean Fields), also suffered several landslide crises in weathered volcanoclastic rocks as a consequence of intense rainfalls or wildfires. To identify slope failures phenomena occurred in the winter season 2019–2020 an innovative procedure has been proposed. The purpose of this procedure is to highlight areas where major land cover changes occurred within our area of study, which can be potentially related to mass movements. The amplitude of spaceborne SAR images has been exploited for the change detection analysis and the output derived from the segmentation procedure has been compared with field observations. The amplitude-based method has been already applied in the detection of landslides, but never on the event with limited extensions, such as for this application. The achieved outcomes allowed the mapping of 62 new landslides that have been used to update the current landslide inventory database. This type of information is expected to help decision-makers with land planning and risk assessment

Rockfall threatening cumae archeological site fruition (Phlegraean fields park—naples)

Natural hazards threaten many archaeological sites in the world; therefore, susceptibility analysis is essential to reduce their impacts and support site fruition by visitors. In this paper, rockfall susceptibility analysis of the western slope of the Cumae Mount in the Cumae Archaeological Site (Phlegraean Fields, Naples), already affected by rockfall events, is described as support to a management plan for fruition and site conservation. Being the first Greek settlement in southern Italy, the site has great historical importance and offers unique historical elements such as the Cumaean Sibyl’s Cave. The analysis began with a 3D modeling of the slope through digital terrestrial photogrammetry, which forms a basis for a geomechanical analysis. Digital discontinuity measurements and cluster analysis provide data for kinematic analysis, which pointed out the planar, wedge and toppling failure potential. Subsequently, a propagation-based susceptibility analysis was completed into a GIS environment: it shows that most of the western sector of the site is susceptible to rockfall, including the access course, a segment of the Cumana Railroad and its local station. The work highlights the need for specific mitigation measures to increase visitor safety and the efficacy of filed-based digital reconstruction to support susceptibility analysis in rockfall prone areas

Anthropogenic sinkholes of the city of Naples, Italy: an update

In recent years, the study of anthropogenic sinkholes in densely urbanized areas has attracted the attention of both researchers and land management entities. The city of Naples (Italy) has been frequently affected by processes generating such landforms in the last decades: for this reason, an update of the sinkhole inventory and a preliminary susceptibility estimation are proposed in this work. Starting from previous data, not modified since 2010, a total of 270 new events occurred in the period February 2010–June 2021 were collected through the examination of online newspapers, local daily reports, council chronicle news and field surveys. The final consistence of the updated inventory is of 458 events occurred between 1880 and 2021, distributed through time with an increasing trend in frequency. Spatial analysis of sinkholes indicates a concentration in the central sector of the city, corresponding to its ancient and historic centre, crossed by a dense network of underground tunnels and cavities. Cavity-roof collapse is confirmed as one of the potential genetic types, along with processes related to rainfall events and service lines damage. A clear correlation between monthly rainfall and the number of triggered sinkholes was identified. Finally, a preliminary sinkhole susceptibility assessment, carried out by Frequency Ratio method, confirms the central sector of city as that most susceptible to sinkholes and emphasizes the predisposing role of service lines, mostly in the outermost areas of the city