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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 6
Geosci. Model Dev., 7, 2969–2982, 2014
https://doi.org/10.5194/gmd-7-2969-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 2969–2982, 2014
https://doi.org/10.5194/gmd-7-2969-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 15 Dec 2014

Development and technical paper | 15 Dec 2014

A strategy for GIS-based 3-D slope stability modelling over large areas

M. Mergili et al.

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Agarwal, D., Puri, S., He, X., and Prasad, S. K.: Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations, High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion, 10–16 November 2012, 2012.
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., and Saltz, J.: Hadoop GIS: a high performance spatial data warehousing system over mapreduce, Proceedings of the VLDB Endowment, 6, 1009–1020, 2013.
Alvioli, M., Marchesini, I., Rossi, M., Santangelo, M., Cardinali, M., Reichenbach, P., Ardizzone, F., Fiorucci, F., Balducci, V., Mondini, A. C., and Guzzetti, F.: Parallel processing in WPS services for geological and geomorphological mapping, 8th IAG International Conference on Geomorphology Paris, 27–31 August 2013, 2013.
Alvioli, M., Rossi, M., and Guzzetti, F.: Scaling properties of rainfall-induced landslides predicted by a physically based model, Geomorphology, 213, 38–47, 2014.
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.
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The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
The article deals with strategies to (i) reduce computation time and to (ii) appropriately...
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