A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Assessing landslide susceptibility using Bayesian probability-based weight of evidence model
Abstract This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W+ and W−) was used as weight for each factor’s type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %.
Assessing landslide susceptibility using Bayesian probability-based weight of evidence model
Abstract This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W+ and W−) was used as weight for each factor’s type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %.
Assessing landslide susceptibility using Bayesian probability-based weight of evidence model
Sujatha, Evangelin Ramani (author) / Kumaravel, P. (author) / Rajamanickam, G. Victor (author)
2013
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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