Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill-interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill-interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill-interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill-interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill-interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas that are most susceptible to water erosion and best allocate resources for soil conservation strategies.

Angileri, S., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., & Agnesi, V. (2016). Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy). GEOMORPHOLOGY, 262, 61-76 [10.1016/j.geomorph.2016.03.018].

Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)

ANGILERI, Silvia Eleonora
;
CONOSCENTI, Christian;ROTIGLIANO, Edoardo;AGNESI, Valerio
2016

Abstract

Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill-interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill-interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill-interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill-interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill-interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas that are most susceptible to water erosion and best allocate resources for soil conservation strategies.
Settore GEO/04 - Geografia Fisica E Geomorfologia
Settore GEO/05 - Geologia Applicata
Angileri, S., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., & Agnesi, V. (2016). Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy). GEOMORPHOLOGY, 262, 61-76 [10.1016/j.geomorph.2016.03.018].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/191434
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