Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSpatial analysis of clay content in soils using neurocomputing and pedological support: a case study of Valle Telesina (South Italy)
Anno di pubblicazione2016
Formato
  • Elettronico
  • Cartaceo
Autore/iGiuliano Langella Angelo Basile Antonello Bonfante Florindo Antonio Mileti Fabio Terribile
Affiliazioni autoriGiuliano Langella (CNR) Angelo Basile (CNR) Antonello Bonfante (CNR) Florindo Antonio Mileti (UNINA) Fabio Terribile (UNINA)
Autori CNR e affiliazioni
  • GIULIANO LANGELLA
  • ANGELO BASILE
  • ANTONELLO BONFANTE
Lingua/e
  • inglese
AbstractThe spatial analysis of soil properties by means of quantitative methods is useful to make predictions at sampled and unsampled locations. Two most important characteristics are tackled, namely the option of using complex and nonlinear models in contrast with (also very simple) linear approaches, and the opportunity to build spatial inference tools using horizons as basic soil components. The objective is to perform the spatial analysis of clay content for validation purposes in order to understand whether nonlinear methods can manage soil horizons, and to quantitatively measure how much they outperform simpler methods. This is addressed in a case study in which relatively few records are available to calibrate (train) such complex models. We built three models which are based on artificial neural networks, namely single artificial neural networks, median neural networks and bootstrap aggregating neural networks with genetic algorithms and principal component regression (BAGAP). We perform a validation procedure at three different levels of soil horizon aggregations (i.e. topsoil, profile and horizon pedological supports). The results show that neurocomputing performs best at any level of pedological support even when we use an ensemble of neural nets (i.e. BAGAP), which is very data intensive. BAGAP has the lowest RMSE at any level of pedological support with RMSE (Topsoil) = 7.2%, RMSE (Profile) = 7.8% and RMSE (Horizon) = 8.8%. We analysed in-depth artificial neural parameters, and included them in the ''Appendix'', to provide the best tuned neuralbased model to enable us to make suitable spatial predictions.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1
Pagine a19
Pagine totali19
RivistaEnvironmental earth sciences (Internet)
Attiva dal 2009
Editore: Springer - Berlin
Paese di pubblicazione: Germania
Lingua: inglese
ISSN: 1866-6299
Titolo chiave: Environmental earth sciences (Internet)
Titolo proprio: Environmental earth sciences. (Internet)
Titolo abbreviato: Environ. earth sci. (Internet)
Numero volume della rivista75
Fascicolo della rivista-
DOI10.1007/s12665-016-6163-7
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveArtificial neural network, Pedological support, Soil variability, Spatial analysis, Bagging
Link (URL, URI)http://link.springer.com/article/10.1007/s12665-016-6163-7
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione15/10/2016
Note/Altre informazioniSupplementary materials are provided at the following link: http://link.springer.com/article/10.1007/s12665-016-6163-7
Strutture CNR
  • ISAFoM — Istituto per i sistemi agricoli e forestali del mediterraneo
Moduli/Attività/Sottoprogetti CNR
  • AG.P04.019.001 : Vulnerabilità del territorio agricolo e forestale all'uso ed agli stress abiotici
Progetti Europei-
Allegati
2016_EES_Langella et al_clay and neurocomputing.pdf (documento privato )
Descrizione: PDF del manoscritto
Tipo documento: application/pdf

Dati associati a vecchie tipologie
I dati associati a vecchie tipologie non sono modificabili, derivano dal cambiamento della tipologia di prodotto e hanno solo valore storico.
Citazione bibliograficaLangella, G., Basile, A., Bonfante, A. et al. Environ Earth Sci (2016) 75: 1357. doi:10.1007/s12665-016-6163-7