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Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

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  • Paolo Magliulo
  • Antonio Di Lisio
  • Filippo Russo
  • Antonio Zelano

Abstract

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements. Copyright Springer Science+Business Media B.V. 2008

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  • Paolo Magliulo & Antonio Di Lisio & Filippo Russo & Antonio Zelano, 2008. "Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 47(3), pages 411-435, December.
  • Handle: RePEc:spr:nathaz:v:47:y:2008:i:3:p:411-435
    DOI: 10.1007/s11069-008-9230-x
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    12. Paraskevas Tsangaratos & Andreas Benardos, 2014. "Estimating landslide susceptibility through a artificial neural network classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1489-1516, December.
    13. N. Sabatakakis & G. Koukis & E. Vassiliades & S. Lainas, 2013. "Landslide susceptibility zonation in Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 523-543, January.
    14. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 883-900, September.
    15. Emre Özþahin, 2015. "Landslide Susceptibility Analysis of Tekirdað City Using Geographic Information Systems (GIS) and Analytic Hierarchy Process (AHP)," Eurasian Academy Of Sciences Social Sciences Journal, Eurasian Academy Of Sciences, vol. 6(6), pages 50-71, November.
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