Mapping the anthropic backfill of the historical center of Rome (Italy) by using Intrinsic Random Functions of Order k (IRF-k) (Articolo in rivista)

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Label
  • Mapping the anthropic backfill of the historical center of Rome (Italy) by using Intrinsic Random Functions of Order k (IRF-k) (Articolo in rivista) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-21928-3_7 (literal)
Alternative label
  • Ciotoli, Giancarlo; Stigliano, Francesco P.; Marconi, Fabrizio; Moscatelli, Massimiliano; Mancini, Marco; Cavinato, Gian Paolo (2011)
    Mapping the anthropic backfill of the historical center of Rome (Italy) by using Intrinsic Random Functions of Order k (IRF-k)
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ciotoli, Giancarlo; Stigliano, Francesco P.; Marconi, Fabrizio; Moscatelli, Massimiliano; Mancini, Marco; Cavinato, Gian Paolo (literal)
Pagina inizio
  • 92 (literal)
Pagina fine
  • 102 (literal)
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  • http://www.scopus.com/record/display.url?eid=2-s2.0-79960296709&origin=inward (literal)
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  • 6782 LNCS (literal)
Rivista
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  • 1 (literal)
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  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Consiglio Nazionale delle Ricerche (literal)
Titolo
  • Mapping the anthropic backfill of the historical center of Rome (Italy) by using Intrinsic Random Functions of Order k (IRF-k) (literal)
Abstract
  • The historical centre of Rome is characterized by the presence of high thickness of anthropic cover with scarce geotechnical characteristics. This anthropic backfill could induce damages in urban areas, i.e. mainly differential settlements and seismic amplifications. About 1400 measurements from boreholes stored in the UrbiSIT database have been used to re-construct the anthropic backfill bottom surface by geostatistical techniques. The Intrinsic Random Functions of order k (IRF-k) was employed and compared with other interpolation methods (i.e. ordinary kriging and kriging with external drift) to determine the best spatial predictor. Furthermore, IRF-k allows to estimate by using an external drift as secondary information. The advantage of this method is that the modeling of the optimal generalized covariance is performed by using an automatic procedure avoiding the time-consuming modeling of the variogram. Furthermore, IRF-k allows the modeling of non stationary variables. © 2011 Springer-Verlag. (literal)
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