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Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy)

DOI: 10.4236/gep.2016.412001, PP. 1-21

Keywords: Landslide Susceptibility, Antrodoco, Conditional Analysis, Rare Events Logistic Regression, Classification Methods

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This paper discusses some methodological aspects for the production of susceptibility maps of slope instability developed within the CARG Project (Geological Cartography of Italy at 1:50,000 scale). It describes an example of a susceptibility map in the presence of low susceptibility, using database having zero or negligible cost, with the aim to test some methodologies that can be easily reproducible to get a first estimate of the landslide susceptibility on a wide area. Two statistical approaches have been applied: the non-parametric conditional analysis and the logistic analysis for rare events. The predictive ability obtained from the two methodologies, was evaluated by the success-prediction curves for the conditional analysis, and by the Receiver Operating Characteristic curve (ROC), for the logistic model. The landslide susceptibility maps have been classified into four classes using both the Natural Breaks algorithm and the method proposed by Chung and Fabbri (2003). The paper considers the influence of these two classification methods on the quality of final results.


[1]  Hammond, C., Hall, D., Miller, S. and Swetik, P. (1992) Level I Stability Analysis (LISA) Documentation for Version 2.0. General Technical Report INT-285, USDA, Forest Service, Intermountain Research Station, Ogden.
[2]  Akbari, A., Yahaya, F., Azamirad, M. and Fanodi, M. (2014) Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools. Electronic Journal of Geotechnical Engineering, 19, 1687-1696.
[3]  Regmi, N.R., Giardino, J.R., McDonald, E.V. and Vitek, J.D. (2014) A Comparison of Logistic Regression-Based Models of Susceptibility to Landslides in Western Colorado, USA. Landslides, 11, 247-262.
[4]  Bai, S.B., Wang, J., Lü, G.N., Zhou, P.G., Hou, S.S. and Xu, S.N. (2010) GIS-Based Logistic Regression for Landslide-Susceptibility Mapping of the Zhongxian Segment in the Three Gorges Area, China. Geomorphology, 115, 23-31.
[5]  Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M. and Ardizzone, F. (2005) Probabilistic Landslide Hazard Assessment at the Basin Scale. Geomorphology, 72, 272-299.
[6]  Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F. and Cardinali, M. (2006) Landslide Hazard Assessment in the Collazzone Area, Umbria, Central Italy. Natural Hazards and Earth System Sciences, 6, 115-131.
[7]  Pradhan, B. (2013) A Comparative Study on the Predictive Ability of the Decision Tree, Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS. Computers & Geosciences, 51, 350-365.
[8]  Lee, S., Jeon, S.W., Oh, K.-Y. and Lee, M.-J. (2016) The Spatial Prediction of Landslide Susceptibility Applying Artificial Neural Network and Logistic Regression Models: A Case Study of Inje, Korea. Open Geosciences, 8, 117-132.
[9]  Falaschi, F., Giacomelli, F., Federici, P.R., Puccinelli, A., D’Amato Avanzi, G., Pochini, A. and Ribolini, A. (2009) Logistic Regression versus Artificial Neural Networks: Landslide Susceptibility Evaluation in a Sample Area of the Serchio River Valley, Italy. Natural Hazards, 50, 551-569.
[10]  Pradhan, B. and Lee, S. (2010) Landslide Susceptibility Assessment and Factor Effect Analysis: Back Propagation Artificial Neural Networks and Their Comparison with Frequency Ratio and Bivariate Logistic Regression Modeling. Environmental Modelling & Software, 25, 747-759.
[11]  D’Ambrogi, C., Pantaloni, M. and Pichezzi, R.M. (2010) I 20 anni del Progetto di cartografia geologica nazionale. Memorie Descrittive della Carta Geologica d’Italia, 88.
[12]  Amanti, A. (2010) Integrazioni geotematiche al rilevamento geologico: Il caso del foglio “Antrodoco”. Memorie Descrittive della Carta Geologica d’Italia, 88, 39-46.
[13]  Amanti, A., Chiessi, V., Guarino, P.M. and Serafini, R. (2010) Landslides Cartography in “foglio Antrodoco” Project: Progress Report and Result. Memorie Descrittive della Carta Geologica d’Italia, 88, 139.
[14]  Brabb, E. (1984) Innovative Approaches for Landslide Hazard Evaluation. IV International Symposium on Landslides, Toronto, 307-323.
[15]  Corominas, J., Leroi, E. and Savage, W.Z. (2008) Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning. Engineering Geology, 102, 85-98.
[16]  Jenks, G.F. and Caspall, F.C. (1971) Error on Choroplethic Maps: Definition, Measurement, Reduction. Annals of the Association of American Geographers, 61, 217-244.
[17]  Chung, C.F. and Fabbri, A.G. (2003) Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards, 30, 451-472.
[18]  Pantaloni, M., Capotorti, F., D’Ambrogi, C. and Di Stefano, R. (2004) Geological Guide of the 348 Antrodoco Sheet ISPRA. Internal Document, ISPRA, Italy.
[19]  Berti, D., et al. (2009) La Geologia del Foglio n. 348 Antrodoco. Memorie Descrittive Carta Geologica d’Italia, 88, 134.
[20]  ISPRA Institute for Environmental Protection and Research (2008) Landslides in Italy, Special Report 2008, 83, ISPRA, Italy.
[21]  SGI Servizio Geologico d’Italia (1955) Carta Geologica d’Italia Scala 1:100,000, Foglio n. 139 L’Aquila.
[22]  Meletti, C. and Montaldo, V. (2007) Stime di pericolosità sismica per diverse probabilità di superamento in 50 anni: Valori di ag, Progetto DPC-INGV S1, Deliverable D2.
[23]  Longley, P.A. and Batty, M. (2003) Advanced Spatial Analysis: The CASA Book of GIS. ESRI, Redlands, CA.
[24]  McCoy, J. (2004) ArcGIS 9 Geoprocessing in ArcGIS. ESRI, Redlands, CA.
[25]  Gumbel, E.J. (1958) Statistics of Extremes. Columbia University Press, New York.
[26]  Carrara, A., Cardinali, M., Guzzetti, F. and Reichenbach, P. (1995) GIS Technology in Mapping Landslide Hazard. In: Carrara, A. and Guzzetti, F., Eds., Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, 135-176.
[27]  Chung, C.F., Fabbri, A.G. and Van Westen, C.J. (1995) Multivariate Regression Analysis for Landslide Hazard Zonation. In: Carrara, A. and Guzzetti, F., Eds., Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, 107-134.
[28]  Van Den Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M. and Poesen, J. (2009) Combined Landslide Inventory and Susceptibility Assessment Based on Different Mapping Units: An Example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9, 507-521.
[29]  Hansen, A. (1984) Landslide Hazard Analysis. In: Brunsen, D. and Prior, D.B., Eds., Slope Instability, John Wiley and Sons, New York, 523-602.
[30]  Kendall, M. and Stuart, A. (1979) The Advanced Theory of Statistics: Inference and Relationship. Hodder Arnold, London.
[31]  Reichenbach, P., Guzzetti, F. and Carrara, A. (2002) Special Issue on Assessing and Mapping Landslide Hazards and Risk. Natural Hazards and Earth Systems Science, 2, 1-2.
[32]  Van Westen, C.J., Rengers, N. and Soeters, R. (2003) Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment. Natural Hazards, 30, 399-419.
[33]  Brenning, A. (2005) Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. Natural Hazards and Earth System Sciences, 5, 853-862.
[34]  Chung, C.F. and Fabbri, A.G. (2008) Predicting Landslides for Risk Analysis Spatial Models Tested by a Cross-Validation Technique. Geomorphology, 94, 438-452.
[35]  King, G. and Zeng, L. (2001) Logistic Regression in Rare Events Data. Political Analysis, 9, 137-163.
[36]  Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G. and Vandekerckhove, L. (2006) Prediction of Landslide Susceptibility Using Rare Events Logistic Regression: A Case-Study in the Flemish Ardennes (Belgium). Geomorphology, 76, 392-410.
[37]  Imai, K., King, G. and Lau, O. (2007) Relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables. In: Imai, K. and King, G., Eds., Zelig: Everyone’s Statistical Software, 493-502.
[38]  R Core Team (2012) R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing. Vienna, Austria.
[39]  Lasko, T.A., Bhagwat, J.G., Zou, K.H. and Ohno-Machado, L. (2005) The Use of Receiver Operating Characteristic Curves in Biomedical Informatics. Journal of Biomedical Informatics, 38, 404-415.
[40]  Begueria, S. (2006) Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management. Natural Hazard, 37, 315-329.
[41]  Fawcett, T. (2006) An Introduction to ROC Analysis. Pattern Recognition Letters, 27, 861-874.
[42]  Petschko1, H., Brenning, A., Bell, R., Goetz1, J., and Glade, T. (2014) Assessing the Quality of Landslide Susceptibility Maps—Case Study Lower Austria. Natural Hazards and Earth System Sciences, 14, 95-118.
[43]  Chalkias, C., Ferentinou, M. and Polykretis, C. (2014) GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece. Geosciences, 4, 176-190.


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