1,721,042 research outputs found
A NEW PERSPECTIVE FOR REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT
Full text linkLandslides pose a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk in territorial planning or investigating landscape evolution. In the case of the Italian territory, several landslide hazard and risk maps were produced ranging from regional to national scale. This was made possible leveraging public domain data of the Italian Landslide Inventory (IFFI project; Trigila et alii, 2010), or other geodatabases spanning from local to regional scale. However, the practical utility of this inventory is often limited in many applications due to its spatial inhomogeneity or the use of different mapping methods and classification criteria. Despite the impressive advancements in techniques for assessing natural hazard susceptibility at a national scale over the past years, including statistical models, AI based models (i.e. Neural Networks) and others, the results are still limited by the quality of the data used. Specifically, the effectiveness of these models is closely tied to the quality of the landslide inventory utilized. Currently, recent regional landslide inventories could potentially enhance precision and accuracy compared to the national dataset, primarily owing to their finer resolution compared to the IFFI dataset. In this work, we present a new approach to assess landslide susceptibility at local scale, relying on regional landslide inventories. Using a data-driven technique, we propose to train a single model on a landslide inventory consisting of a composition of regional inventories selected to be representative of the national scenario. The weighted model is now capable of predicting landslide susceptibility in any study area across Italy. The entire analysis has been done using the SRT tool for Google Earth Engine and the SZ-plugin for QGIS. All the data used and processed are freely available and downloadable. The proposed approach has been tested in the framework of the PNRR RETURN project. The evaluation was conducted in two specific areas: the first one encompasses a section of the railway connecting Napoli to Bari (southern Italy), while the second focuses on areas impacted by the Marche region 2022 landslide event (central Italy). © Author(s). All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Seismic noise monitoring of a small rock block collapse test
Full text linkWe tested the capability of seismic noise to monitor the stability conditions of a small rock block that we forced to fail in four following stages. Ambient vibrations were recorded with a broad-band 3C seismometer placed on top of the block and were processed to analyse their spectral and polarization characteristics with diverse algorithms. To analyse the spectral content of the records, we applied the multitaper method while seismic noise polarization features were investigated by means of the singular value decomposition of the Hermitian spectral density matrix. Numerical modelling was found to add limited value because of the uncertainty in estimating correctly spatial and mechanical features of the rock bridges between the block and the rock mass. Nevertheless, a modelling exercise we performed is in agreement with previous post-failure observations according to which unstable rocks may be coupled to the stable rock mass by rock bridges covering only a few per cent of the total surface of the fractures. Our analyses confirm that, when approaching final collapse, there is a trend of the block eigenmodes towards lower frequencies and show that polarized bands become narrower
Comparison of shallow landslide stability models through laboratory simulations
No full textShallow mass movements represent a significant portion within the vast family of landslide phenomena. Moreover, their detection, monitoring and stability analysis is not an easy task. Analytical stability models are often used for the purpose. Their formulation ranges from extremely simple ones to rather complex formulations which include a number of physical parameters intrinsic to the terrain as well as external forcing agents. The application of such models seems to be strictly site-specific as various authors calibrate their use to a particular case study under investigation. In this work we propose a comparison on the application of several models for shallow mass movement stability analysis. Even though their formulation is similar, the significance of the governing parameter in each model varies and thus application of those models on the same case study leads to greatly varying results. The analytical models have been testes at the laboratory scale under several types of shallow landslide simulations. The obtained results indicate that often simpler models are able to successfully predict instability conditions in terms of factor of safety. On the other hand, a variation of the homogeneity of the terrain poses a series of difficulties in the implementation of a given model. Therefore, a discussion on the applicability of such models on real case studies is proposed, which highlights the limits of their reliability
Snow Melting Experimental Analysis on a Downscaled Shallow Landslide: A Focus on the Seepage Activity of the Snow–Soil System
No full textThe stability of slopes is influenced by seasonal variations in thermal, hydrological, and mechanical processes. This study investigates the role of snowmelt in triggering shallow landslides through controlled laboratory experiments simulating winter, spring, and summer conditions. Snowpack dynamics and water movement were analyzed to understand filtration, infiltration, and runoff mechanisms. The results show that during winter, snow acts as a protective layer, slowing infiltration through its insulating and loading effects. In spring, rising temperatures melt snow, increasing water infiltration and filtration, accelerating soil saturation, and triggering slope failures. Summer rainfall-induced landslides exhibit distinct mechanisms, driven by progressive saturation. The transition from winter to spring highlights a critical phase where snowmelt interacts with warmer soils, intensifying slope instability risks. Numerical simulations using HYDRUS 1D validated the experimental findings, demonstrating its utility in modeling infiltration under varying thermal gradients. This study underscores the importance of incorporating snowmelt dynamics into landslide risk assessments and early warning systems, particularly as climate change accelerates snowmelt cycles in mountainous regions. These findings provide essential insights into seasonal variations in collapse mechanisms, emphasizing the need for further research to address the increasing impact of snowmelt in shallow landslides
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