A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing

Martire, Diego Di; Paci, Marida; Confuorto, Pierluigi; Costabile, Salvatore; Guastaferro, Francesca; Verta, Angelo; Calcaterra, Domenico

doi:10.1016/j.jag.2017.07.018

Cited by 54 publications

(35 citation statements)

References 47 publications

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“…For instance, about 15,000 landslides happened in China between 2015 and 2017, which caused the economic loss of about one million US dollars. Therefore, it is urgent to conduct studies on landslide susceptibility mapping (LSM) and it is a primary and practical tool for mitigation and risk assessment [3].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

et al. 2019

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The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

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Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

et al. 2019

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“…The validation process of PSI database produced by the Not-Ordinary Plan of Environmental Remote Sensing (PST-A) have been discussed in Di Martire et al [11], while that referring to the Tellus project have been discussed in Vilardo et al [5,14,21].…”

Section: Persistent Scatterer Interferometry (Psi) Datasets Availablementioning

confidence: 99%

Analysis and Classification of Natural and Human-Induced Ground Deformations at Regional Scale (Campania, Italy) Detected by Satellite Synthetic-Aperture Radar Interferometry Archive Datasets

Matano

2019

Remote Sensing

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The high levels of geo-hydrological, seismic, and volcanic hazards in the Campania region prompted full data collection from C-band satellites ERS-1/2, ENVISAT, and RADARSAT within regional (TELLUS) and national (PST-A) projects. The quantitative analysis, interpretation, and classification of natural and human-induced slow-rate ground deformations across a span of two decades (1992–2010) was performed at regional scale (Campania, Italy) by using interferometric archive datasets, based on the Persistent Scatterer Interferometry approach. As radar satellite sensors have a side-looking view, the post-processing of the interferometric datasets allows for the evaluation of two spatial components (vertical and E-W horizontal ones) of ground deformation, while the N-S horizontal component cannot be detected. The ground deformation components have been analyzed across 89.5% of the Campania territory within a variety of environmental, topographical, and geological conditions. The main part (57%) of the regional territory was characterized during 1992–2010 by stable areas, where SAR signals do not have recorded significant horizontal and vertical components of ground deformation with an average annual rate greater than +1 mm/yr or lower than −1 mm/yr. Within the deforming areas, the coastal plains are characterized by widespread and continuous strong subsidence signals due to sediment compaction locally enhanced by human activity, while the inner plain sectors show mainly scattered spots with locally high subsidence in correspondence of urban areas, sinkholes, and groundwater withdrawals. The volcanic sectors show interplaying horizontal and vertical trends due to volcano-tectonic processes, while in the hilly and mountain inner sectors the ground deformation is mainly controlled by large-scale tectonic activity and by local landslide activity. The groundwater-related deformation is the dominant cause of human-caused ground deformation. The results confirm the importance of using Persistent Scatterer Interferometry data for a comprehensive understanding of rates and patterns of recent ground deformation at regional scale also within tectonically active areas as in Campania region.

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“…In particular, the availability of high resolution and short repeat time satellite constellations lead to more effective near real-time disaster monitoring, assessment response and greater ability to constrain dynamically changing physical processes [3,4]. Although many studies have been carried out using InSAR in the field of structural monitoring looking at urban areas [5][6][7], bridges [8,9], railways [10,11], tunneling [12][13][14][15], and dams [16][17][18][19], state of the art scientific literature sees only few papers coupling MT-InSAR and structural modeling as a building damage assessment tool [6,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study

et al. 2018

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Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality led to more effective, near real time assessment and response, and a greater ability of constraining dynamically changing physical processes. Using examples of the COSMO-SkyMed (CSK) system, we present a methodology that bridges the gaps between MT-InSAR and the relative stiffness method for tunnel-induced subsidence damage assessment. The results allow quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (~71% of the analyzed buildings), overestimating tensile strains up to 50%. With this work we show how these two techniques in the field of remote sensing and structural engineering can be synergistically used to complement and replace the traditional ground based analysis by providing an extended coverage and a temporally dense set of data.

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A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing

Cited by 54 publications

References 47 publications

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Analysis and Classification of Natural and Human-Induced Ground Deformations at Regional Scale (Campania, Italy) Detected by Satellite Synthetic-Aperture Radar Interferometry Archive Datasets

Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study

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