Abstract
The principal aim of this study is to compare different landslide susceptibility zonation models for predicting areas prone to shallow landsliding using both physically distributed landslide models and artificial neural networks. Necessary geotechnical and hydrological parameters were obtained coupling sample laboratory analysis and in situ measures; soil thickness was estimated using an empirical model while distribution of rainfall intensity was analyzed by performing a spatial interpolation. The predictive capabilities of these models were finally evaluated using a threshold-independent quantitative method (the ROC plot).
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Acknowledgments
This study has been supported by research projects funded by Broni municipality, Pavia Province and Rotary Club Oltrepo Pavese.
The aerial photographs of 18 May 2009 were taken by Ditta Rossi s.r.l. (Brescia).
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Meisina, C., Zizioli, D., Zucca, F. (2013). Methods for Shallow Landslides Susceptibility Mapping: An Example in Oltrepo Pavese. In: Margottini, C., Canuti, P., Sassa, K. (eds) Landslide Science and Practice. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31325-7_58
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DOI: https://doi.org/10.1007/978-3-642-31325-7_58
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