Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2791-2022
https://doi.org/10.5194/gmd-15-2791-2022
Development and technical paper
 | 
06 Apr 2022
Development and technical paper |  | 06 Apr 2022

Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: a new modular toolkit for landslide susceptibility assessment

Jewgenij Torizin, Nick Schüßler, and Michael Fuchs

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Cited articles

Agterberg, F. P. and Cheng, Q.: Conditional independence Test for Weight-of-Evidence Modeling, Nat. Resour. Res., 11, 249–255, https://doi.org/10.1023/A:1021193827501, 2002. 
Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review and new perspectives, Bull. Eng. Geol. Envir., 58, 21–44, https://doi.org/10.1007/s100640050066, 1999. 
Alimohammadlou, Y., Najafi, A., and Gokceoglu, C.: Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran. Catena, 120, 149–162, https://doi.org/10.1016/j.catena.2014.04.009, 2014. 
Balzer, D., Dommaschk, P., Ehret, D., Fuchs, M., Glaser, S., Henscheid, S., Kuhn, D., Strauß, R., Torizin, J., and Wiedenmann, J.: Massenbewegungen in Deutschland (MBiD) – Beiträge zur Modellierung der Hangrutschungsempfindlichkeit. Ein Kooperationsprojekt zwischen den Staatlichen Geologischen Diensten der Bundesländer Baden-Württemberg, Bayern, Nordrhein-Westfalen, Sachsen und der Bundesanstalt für Geowissenschaften und Rohstoffe im Auftrag des Direktorenkreises der Staatlichen Geologischen Dienste in Deutschland, Abschlussbericht, Augsburg, Freiberg, Freiburg, Hannover und Krefeld, https://www.bgr.bund.de/DE/Themen/Erdbeben-Gefaehrdungsanalysen/Downloads/igga_mbid_abschlussbericht.html?nn=1542304 (last access: 31 March 2022), 2020. 
Barbieri, G. and Cambuli, P.: The weight of evidence statistical method in landslide susceptibility mapping 424 of the Rio Pardu Valley (Sardinia, Italy), 18th World IMACS/MODSIM Congress, Cairns, Australia, 2009. 
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Short summary
With LSAT PM we introduce an open-source, stand-alone, easy-to-use application that supports scientific principles of openness, knowledge integrity, and replicability. Doing so, we want to share our experience in the implementation of heuristic and data-driven landslide susceptibility assessment methods such as analytic hierarchy process, weights of evidence, logistic regression, and artificial neural networks. A test dataset is available.
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