Articles | Volume 20, issue 1
https://doi.org/10.5194/nhess-20-271-2020
https://doi.org/10.5194/nhess-20-271-2020
Research article
 | 
22 Jan 2020
Research article |  | 22 Jan 2020

Dynamic path-dependent landslide susceptibility modelling

Jalal Samia, Arnaud Temme, Arnold Bregt, Jakob Wallinga, Fausto Guzzetti, and Francesca Ardizzone

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Cited articles

Akaike, H.: Information theory and an extension of the maximum likelihood principle, in: Selected Papers of Hirotugu Akaike, Springer, New York, 199–213, 1998. 
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling, Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, 2016. 
Ardizzone, F., Cardinali, M., Galli, M., Guzzetti, F., and Reichenbach, P.: Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar, Nat. Hazards Earth Syst. Sci., 7, 637–650, https://doi.org/10.5194/nhess-7-637-2007, 2007. 
Ardizzone, F., Fiorucci, F., Santangelo, M., Cardinali, M., Mondini, A. C., Rossi, M., Reichenbach, P., and Guzzetti, F.: Very-high resolution stereoscopic satellite images for landslide mapping, in: Landslide science and practice, Springer, Berlin, Heidelberg, 95–101, 2013. 
Ardizzone, F., Fiorucci, F., Mondini, A. C., and Guzzetti, F.: TXT-tool 1.039-1.1: Very-High Resolution Stereo Satellite Images for Landslide Mapping, in: Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools: Volume 1: Fundamentals, Mapping and Monitoring, edited by: Sassa, K., Guzzetti, F., Yamagishi, H., Arbanas, Ž., Casagli, N., McSaveney, M., and Dang, K., Springer International Publishing, Cham, 83–94, 2018. 
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Short summary
For the Collazzone study area in Italy, we quantified how much landslides follow others using Ripley's K function, finding that susceptibility is increased within 60 m and 17 years after a previous landslide. We then calculated the increased susceptibility for every pixel and for the 17-time-slice landslide inventory. We used these as additional explanatory variables in susceptibility modelling. Model performance increased substantially with this landslide history component included.
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