Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year 2.693
  • CiteScore value: 2.43 CiteScore 2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 73 Scimago H index 73
Volume 16, issue 12 | Copyright
Nat. Hazards Earth Syst. Sci., 16, 2729-2745, 2016
https://doi.org/10.5194/nhess-16-2729-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Dec 2016

Research article | 16 Dec 2016

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Stefan Steger et al.
Related authors
Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave
Milan Flach, Sebastian Sippel, Fabian Gans, Ana Bastos, Alexander Brenning, Markus Reichstein, and Miguel D. Mahecha
Biogeosciences, 15, 6067-6085, https://doi.org/10.5194/bg-15-6067-2018,https://doi.org/10.5194/bg-15-6067-2018, 2018
Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology
Ekrem Canli, Martin Mergili, Benni Thiebes, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 18, 2183-2202, https://doi.org/10.5194/nhess-18-2183-2018,https://doi.org/10.5194/nhess-18-2183-2018, 2018
Inferring the destabilization susceptibility of mountain permafrost in the French Alps using an inventory of destabilized rock glaciers
Marco Marcer, Charlie Serrano, Alexander Brenning, Xavier Bodin, Jason Goetz, and Philippe Schoeneich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-97,https://doi.org/10.5194/tc-2018-97, 2018
Revised manuscript under review for TC
Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, and Miguel D. Mahecha
Earth Syst. Dynam., 8, 677-696, https://doi.org/10.5194/esd-8-677-2017,https://doi.org/10.5194/esd-8-677-2017, 2017
Editorial to the special issue on resilience and vulnerability assessments in natural hazard and risk analysis
Sven Fuchs, Margreth Keiler, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 17, 1203-1206, https://doi.org/10.5194/nhess-17-1203-2017,https://doi.org/10.5194/nhess-17-1203-2017, 2017
Related subject area
Landslides and Debris Flows Hazards
Large-scale physical modelling study of a flexible barrier under the impact of granular flows
Dao-Yuan Tan, Jian-Hua Yin, Wei-Qiang Feng, Jie-Qiong Qin, and Zhuo-Hui Zhu
Nat. Hazards Earth Syst. Sci., 18, 2625-2640, https://doi.org/10.5194/nhess-18-2625-2018,https://doi.org/10.5194/nhess-18-2625-2018, 2018
Effective surveyed area and its role in statistical landslide susceptibility assessments
Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli
Nat. Hazards Earth Syst. Sci., 18, 2455-2469, https://doi.org/10.5194/nhess-18-2455-2018,https://doi.org/10.5194/nhess-18-2455-2018, 2018
Rainfall events with shallow landslides in the Entella catchment, Liguria, northern Italy
Anna Roccati, Francesco Faccini, Fabio Luino, Laura Turconi, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 18, 2367-2386, https://doi.org/10.5194/nhess-18-2367-2018,https://doi.org/10.5194/nhess-18-2367-2018, 2018
Evaluation of predictive models for post-fire debris flow occurrence in the western United States
Efthymios I. Nikolopoulos, Elisa Destro, Md Abul Ehsan Bhuiyan, Marco Borga, and Emmanouil N. Anagnostou
Nat. Hazards Earth Syst. Sci., 18, 2331-2343, https://doi.org/10.5194/nhess-18-2331-2018,https://doi.org/10.5194/nhess-18-2331-2018, 2018
Combining temporal 3-D remote sensing data with spatial rockfall simulations for improved understanding of hazardous slopes within rail corridors
Megan van Veen, D. Jean Hutchinson, David A. Bonneau, Zac Sala, Matthew Ondercin, and Matt Lato
Nat. Hazards Earth Syst. Sci., 18, 2295-2308, https://doi.org/10.5194/nhess-18-2295-2018,https://doi.org/10.5194/nhess-18-2295-2018, 2018
Cited articles
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., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002.
Atkinson, P. M. and Massari, R.: Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy, Comput. Geosci., 24, 373–385, 1998.
Atkinson, P., Jiskoot, H., Massari, R., and Murray, T.: Generalized linear modelling in geomorphology, Earth Surf. Proc. Land., 23, 1185–1195, 1998.
Ballabio, C. and Sterlacchini, S.: Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy, Math. Geosci., 44, 47–70, https://doi.org/10.1007/s11004-011-9379-9, 2012.
Publications Copernicus
Download
Short summary
This study investigates the propagation of landslide inventory-based positional errors into statistical landslide susceptibility models by artificially introducing such spatial inaccuracies. The findings highlight that (i) an increasing positional error is related to increasing distortions of modelling and validation results, (ii) interrelations between inventory-based errors and modelling results are complex, and (iii) inventory-based errors can be counteracted by adapting the study design.
This study investigates the propagation of landslide inventory-based positional errors into...
Citation
Share