Investigation of the Luco Dei Marsi DSGSD Revealing the First Evidence of a Basal Shear Zone in the Central Apennine Belt (Italy)

Luzio, Emiliano Di; Emanuele, Discenza Marco; Martire, Diego Di; Putignano, Maria Luisa; Mariacarmela, Minnillo; Esposito, Carlo; Mugnozza, Gabriele Scarascia

doi:10.2139/ssrn.3929877

Cited by 1 publication

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“…The city of Moio della Civitella (Salerno Province) is among the sites with the greatest concentration of landslides in the world, which damaged its urban settlement (Infante, 2019), (Di Martire, 2015). A number of factors contribute to the difficulty of predicting landslides and mapping them under settlement cover, including slow movement, human intervention, and not considering the interdependence between geological and geomorphological features when entering as features to Machine Learning and Deep Learning Algorithms (MLA & DLA) (Di Luzio, 2022). By analyzing and predicting geological hazards, these severe effects can be mitigated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Deformation Caused by Landslides Based on Graph Convolution Networks Algorithm and Dinsar Technique

Khalili

Guerriero²,

Pouralizadeh³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%