Abstract
Landslide susceptibility maps are effective tools for the mitigation of risks caused by such geological events. In line with recent scientific trends and thanks to the availability of detailed geological data, landslide susceptibility modeling, by means of statistical methodologies, has gained increasing consideration. The present work is based on a methodology widely employed in the field of ecology to draw prediction maps for the occurrence probability of certain species (MaxEnt). The study area is located in Palma Campania, a town sited in the peri-vesuvian area (in the province of Napoli, southern Italy) and characterized by a significant presence of pyroclastic soils, affected by several landslide events, one of which killed eight people in 1986. In this work, eleven geomorphological and geological predisposing factors were selected, based on previous experiences of landslides in peri-vesuvian areas and following several field surveys. Results were critically evaluated using a validation dataset (Receiver Operating Characteristic—ROC curves), by means of Sensitivity-Specificity graphs estimating Area Under Curve (AUC), and other tests such as the Jackknife and response curves, which highlighted the major role played by a number of factors. The consistent agreement between our results and the existing official map demonstrates the validity of the adopted procedure for emergency and land planning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Calcaterra, D., Parise, M., Palma, B.: Combining historical and geological data for the assessment of the landslide hazard: a case study from Campania, Italy. Nat. Hazards Earth Syst. Sci. 3(1/2), 3–16 (2003)
Carotenuto, F., Angrisani, A.C., Bakthiari, A., Carratù, M.T., Di Martire, D., Finicelli, G.F., Raia, P., Calcaterra, D.: A new statistical approach for landslide susceptibility assessment in the urban area of Napoli (Italy). In: Proceedings of the 4th World Landslide Forum, Ljubljana, Slovenia, vol. 2, pp. 881–889, May 29–June 2, 2017. Springer International Publishing, switzerland (2017). https://doi.org/10.1007/978-3-319-53498-5_100
Carratù, M.T., Di Martire, D., Finicelli, G.F., Calcaterra, D.: Comparison of bivariate and multivariate analyses for landslide susceptibility mapping in the Phlegraean district: the case study of Camaldoli hill (Napoli, Italy). Rend. Online Soc. Geol. It. 35, 50–53 (2015). https://doi.org/10.3301/ROL.2015.61
Conforti, M., Pascale, S., Robustelli, G., Sdao, F.: Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113, 236–250 (2014). https://doi.org/10.1016/j.catena.2013.08.006
Convertino, M., Troccoli, A., Catani, F.: Detecting fingerprints of landslide drivers: a MaxEnt model. J. Geophys. Res. Earth Surf. 118, 1367–1386 (2013). https://doi.org/10.1002/jgrf.20099
Cruden, D.M., Varnes, D.J.: Landslide types and processes. In: Turner, A.K., Schuster, R.L. (eds.) Landslides: Investigation and Mitigation, vol. 247, pp. 36–75. Transportation Research Board, National Research Council, National Academy Press, Washington, DC, Special report (1996)
Davis, J., Blesius, L.: A hybrid physical and maximum-entropy landslide susceptibility model. Entropy 17, 4271–4292 (2015). https://doi.org/10.3390/e17064271
De Vita, P., Agrello, D., Ambrosino, F.: Landslide susceptibility assessment in ash-fall pyroclastic deposits surrounding Mount Somma-Vesuvius: application of geophysical surveys for soil thickness mapping. J. Appl. Geophys. 59(2), 126–139 (2006). https://doi.org/10.1016/j.jappgeo.2005.09.001
Felicísimo, Á.M., Cuartero, A., Remondo, J., Quirós, E.: Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2), 175–189 (2013). https://doi.org/10.1007/s10346-012-0320-1
Finicelli, G.F., Confuorto, P., Carratù, M.T., Di Martire, D.: Multivariate statistical approach vs. deterministic physically-based model for landslide susceptibility assessment. Rend. Online Soc. Geol. It. 41, 151–154 (2016). https://doi.org/10.3301/ROL.2016.116
Guadagno, F.M., Palmieri, M., Siviero, V., Vallario, A.: The Palma Campania landslide. Geol. Tec. 4, 18–29 (1988). (in Italian)
Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M., Chang, K.T.: Landslide inventory maps: new tools for an old problem. Earth Sci. Rev. 112(1), 42–66 (2012). https://doi.org/10.1016/j.earscirev.2012.02.001
Hussin, H.Y., Zumpano, V., Reichenbach, P., Sterlacchini, S., Micu, M., van Westen, C., Bălteanu, D.: Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253, 508–523 (2016). https://doi.org/10.1016/j.geomorph.2015.10.030
Hydrogeomorphological Setting Plan: Update year 2015, Naples, Italy (2015) (in Italian)
Jenks, G.F.: The data model concept in statistical mapping. Int. Yearb. Cartography 7, 186–190 (1967)
Kornejady, A., Ownegh, M., Bahremand, A.: Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152, 144–162 (2017). https://doi.org/10.1016/j.catena.2017.01.010
Municipality Emergency Plan of Palma Campania town (2016)
Phillips, S.J., Anderson, R.P., Schapire, R.E.: Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3), 231–259 (2006). https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips, S.J., Dudík, M.: Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008). https://doi.org/10.1111/j.0906-7590.2007.5203.x
Zizioli, D., Meisina, C., Valentino, R., Montrasio, L.: Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy. Nat. Hazards Earth Syst. Sci. 13(3), 559–573 (2013). https://doi.org/10.5194/nhess-13-559-2013
Aknowledgments
Authors would like to thank reviewers who helped and contributed to the improvement of the text.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sepe, C., Confuorto, P., Angrisani, A.C., Di Martire, D., Di Napoli, M., Calcaterra, D. (2019). Application of a Statistical Approach to Landslide Susceptibility Map Generation in Urban Settings. In: Shakoor, A., Cato, K. (eds) IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018 - Volume 1. Springer, Cham. https://doi.org/10.1007/978-3-319-93124-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-93124-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93123-4
Online ISBN: 978-3-319-93124-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)