Landslides are one of the most widespread natural hazards that cause damage to both property and life everyyear, and therefore, the spatial distribution of the landslide susceptibility is necessary for planning future developmentalactivities. In this paper the artificial neural network (ANN) technique is tested for developing a landslidesusceptibility map in Turbolo River catchment, North Calabria, South Italy.Landslideswere mapped through air-photo interpretation and field surveys, by identifying both the landslide depletionzones (DZs) and accumulation zones (AZs); and relevant geo-environmental thematic layers pertainingto landslide predisposing factors were generated using air-photo interpretation, field surveys and Geographic InformationSystem (GIS) tools. Ten predisposing factors were related to the occurrence of landslide: lithology,faults, land use, drainage network, and a series of topographic factors: elevation, slope, aspect, plan curvature, topographicwetness index (TWI) and stream power index (SPI).In order to evaluate and validate landslide susceptibility, the DZs were divided in twogroups using a randompartitionstrategy. The first group (training set) was used to prepare the susceptibility map, employing a backpropagationlearning algorithm in the Idrisi Taiga software. The second group (testing set) was used to validatethe landslide susceptibility model, using the confusion matrix and the receiver operating characteristic (ROC)curve.The susceptibilitymapwas classified into five susceptibility classes: very low, low, moderate, high, and very high.About 46% of the study area falls in high to very high susceptible classes and most of the DZs mapped (87.3%)occur in the same classes.The validation results showed satisfactory agreement between the susceptibilitymap and the DZs locations; over85% of the DZs of the validation set are correctly classified falling in high and very high susceptibility areas. Also,the ROC curve had shown an area under curve (AUC) value of 0.90which demonstrates the robustness and goodreliability of the landslide susceptibilitymodel. According to these results,we conclude that the map produced bythe artificial neural network is reliable and the methodology applied in the study produced high performance,and satisfactory results, which may be useful for land planning policy.

Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)

ROBUSTELLI, Gaetano;
2014

Abstract

Landslides are one of the most widespread natural hazards that cause damage to both property and life everyyear, and therefore, the spatial distribution of the landslide susceptibility is necessary for planning future developmentalactivities. In this paper the artificial neural network (ANN) technique is tested for developing a landslidesusceptibility map in Turbolo River catchment, North Calabria, South Italy.Landslideswere mapped through air-photo interpretation and field surveys, by identifying both the landslide depletionzones (DZs) and accumulation zones (AZs); and relevant geo-environmental thematic layers pertainingto landslide predisposing factors were generated using air-photo interpretation, field surveys and Geographic InformationSystem (GIS) tools. Ten predisposing factors were related to the occurrence of landslide: lithology,faults, land use, drainage network, and a series of topographic factors: elevation, slope, aspect, plan curvature, topographicwetness index (TWI) and stream power index (SPI).In order to evaluate and validate landslide susceptibility, the DZs were divided in twogroups using a randompartitionstrategy. The first group (training set) was used to prepare the susceptibility map, employing a backpropagationlearning algorithm in the Idrisi Taiga software. The second group (testing set) was used to validatethe landslide susceptibility model, using the confusion matrix and the receiver operating characteristic (ROC)curve.The susceptibilitymapwas classified into five susceptibility classes: very low, low, moderate, high, and very high.About 46% of the study area falls in high to very high susceptible classes and most of the DZs mapped (87.3%)occur in the same classes.The validation results showed satisfactory agreement between the susceptibilitymap and the DZs locations; over85% of the DZs of the validation set are correctly classified falling in high and very high susceptibility areas. Also,the ROC curve had shown an area under curve (AUC) value of 0.90which demonstrates the robustness and goodreliability of the landslide susceptibilitymodel. According to these results,we conclude that the map produced bythe artificial neural network is reliable and the methodology applied in the study produced high performance,and satisfactory results, which may be useful for land planning policy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/138655
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