M
Massimo Conforti
Researcher at National Research Council
Publications - 52
Citations - 1710
Massimo Conforti is an academic researcher from National Research Council. The author has contributed to research in topics: Landslide & Soil test. The author has an hindex of 18, co-authored 47 publications receiving 1315 citations. Previous affiliations of Massimo Conforti include University of Calabria.
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Journal ArticleDOI
Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)
TL;DR: In this paper, an artificial neural network (ANN) technique is tested for developing a landslide susceptibility map in Turbolo River catchment, North Calabria, South Italy.
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Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)
TL;DR: In this paper, a geomorphological and bivariate statistical approach to gully erosion susceptibility mapping in the Turbolo stream catchment (northern Calabria, Italy) is presented.
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Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy
TL;DR: In this paper, GIS-aided procedures for the evaluation of gullying susceptibility on a statistical basis were presented, where four models that differ in the types of method (bivariate vs. multivariate), training set sampling (polygon or point-based) and variables (continuous or categorical).
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Studying the relationship between water-induced soil erosion and soil organic matter using Vis–NIR spectroscopy and geomorphological analysis: A case study in southern Italy
Massimo Conforti,Gabriele Buttafuoco,Antonio P. Leone,Pietro P. C. Aucelli,Gaetano Robustelli,Fabio Scarciglia +5 more
TL;DR: In this paper, the spectral reflectance of the soil organic matter (SOM) was used to predict the soil content in the study area, combining with geostatistics for mapping SOM content, and mapping zones affected by water erosion processes.
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Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content
Massimo Conforti,Annamaria Castrignanò,Gaetano Robustelli,Fabio Scarciglia,M. Stelluti,Gabriele Buttafuoco +5 more
TL;DR: In this paper, the authors used partial least square regression (PLSR) with correlated errors for estimating spatially varying SOM content from laboratory-based soil Vis-NIR spectra and producing a continuous map using a geostatistical method.