In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence.

Conoscenti, C., Agnesi, V., Cama, M., Caraballo-Arias, N.A., & Rotigliano, E. (2018). Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity. LAND DEGRADATION & DEVELOPMENT, 29, 724-736 [10.1002/ldr.2772].

Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity

Conoscenti, Christian
;
Agnesi, Valerio;Cama, Mariaelena;Rotigliano, Edoardo
2018

Abstract

In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence.
Settore GEO/04 - Geografia Fisica E Geomorfologia
Settore GEO/05 - Geologia Applicata
Conoscenti, C., Agnesi, V., Cama, M., Caraballo-Arias, N.A., & Rotigliano, E. (2018). Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity. LAND DEGRADATION & DEVELOPMENT, 29, 724-736 [10.1002/ldr.2772].
File in questo prodotto:
File Dimensione Formato  
2018 - Conoscenti et al. - Land Degradation & Development - Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regre.pdf

non disponibili

Tipologia: Versione Editoriale
Dimensione 1.62 MB
Formato Adobe PDF
1.62 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/285898
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 57
  • ???jsp.display-item.citation.isi??? 53
social impact