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Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area
Abstract In this paper, an adaptive neuro-fuzzy modeling (ANFIS) is applied in order to map landslide susceptibility for a Mediterranean catchment (Peloponnese, Greece). The relationship between landslides and factors influencing their occurrence is investigated in GIS environment. Seven conditioning factors, including elevation, slope angle, profile curvature, stream density, distance to main roads, geology, and vegetation were considered in the analysis. Six ANFIS models with different membership functions were developed to generate the corresponding landslide susceptibility maps. The outputs, representing the probability level of landslide occurrence, were grouped into five classes. They were then evaluated using an independent dataset of landslide events in two different validation methods: receiver operating characteristics (ROC) analysis and success and prediction rates. The majority of the calculated area under the curve values for the two validation methods was in the range 0.70–0.90 indicating between fair and very good prediction accuracy for the six models. These values also showed that the prediction accuracy depends on the membership functions examined in the ANFIS modeling. Among these functions, the difference of two sigmoidally shaped (Dsigmf) and product of two sigmoidally shaped (Psigmf) presented the highest prediction accuracy.
Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area
Abstract In this paper, an adaptive neuro-fuzzy modeling (ANFIS) is applied in order to map landslide susceptibility for a Mediterranean catchment (Peloponnese, Greece). The relationship between landslides and factors influencing their occurrence is investigated in GIS environment. Seven conditioning factors, including elevation, slope angle, profile curvature, stream density, distance to main roads, geology, and vegetation were considered in the analysis. Six ANFIS models with different membership functions were developed to generate the corresponding landslide susceptibility maps. The outputs, representing the probability level of landslide occurrence, were grouped into five classes. They were then evaluated using an independent dataset of landslide events in two different validation methods: receiver operating characteristics (ROC) analysis and success and prediction rates. The majority of the calculated area under the curve values for the two validation methods was in the range 0.70–0.90 indicating between fair and very good prediction accuracy for the six models. These values also showed that the prediction accuracy depends on the membership functions examined in the ANFIS modeling. Among these functions, the difference of two sigmoidally shaped (Dsigmf) and product of two sigmoidally shaped (Psigmf) presented the highest prediction accuracy.
Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area
Polykretis, Christos (author) / Chalkias, Christos (author) / Ferentinou, Maria (author)
2017
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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