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Natural Hazards

, Volume 60, Issue 3, pp 937–950 | Cite as

Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania

  • Iuliana Armaş
Original Paper

Abstract

The goal of this paper is to assess the landslide susceptibility of a hilly area in the Subcarpathian sector of the Prahova Valley, using the weight of evidence statistical method. This method aims to reduce the multitude of landslide-related conditions to a pattern of a few discrete predictive variables. The method is based on the decision of which state is more likely to occur grounded on the presence or absence of a predictive variable and the occurrence of an event (e.g., landslide) within a pixel. Based on the chi-square test and the Pearson correlation applied on the data, the selected conditionally independent variables in this study were as follows: slope gradient, slope aspect, and land use. Weights calculated individually for the three themes were added to produce a probability estimate of the area. The predictive power of the map was tested on the basis of a split sample of landslides that were not used in the modeling process. The fact that a great percent of the declivitous surfaces are susceptible to landslides shows the dominant manner of the evolution of the Subcarpathian slopes, the acceleration or deceleration of the process being influenced by the land use.

Keywords

Weights of evidence statistical method Landslide susceptibility map GIS Romanian Subcarpathian area 

Notes

Acknowledgments

This research was financed by the Romanian National University Research Council, through the grant 2916/31GR/2007. The author would like to express her thanks to the two anonymous reviewers for all the careful review of the paper and the valuable suggestions made, aiming a better structure of the ideas and analytical steps.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Geomorphology, Faculty of GeographyUniversity of BucharestBucharestRomania

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