GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society ISSN 2308-1708 Online Edition ISBN 978-3-87907-558-4 Print Edition ISBN 978-3-7001-7826-2 Online Edition
doi:10.1553/giscience2015
GI_Forum, 2015Volume 3 2015, 645 pages Print edition is available at Wichmann-Verlag, Berlin
Elmar Schmaltz,
Hans-Joachim Rosner,
Michael Märker
S. 61 - 71 doi:10.1553/giscience2015s61 Verlag der Österreichischen Akademie der Wissenschaften
Abstract: The objective of this study is the assessment of potential failure zones of landslides in unstable areas. For this purpose, two different stochastic classification models were used: A boosted decision tree approach with TreeNet (TN), and a bagging decision tree approach with Random Forests (RF). Both topographic and soil parameters were considered as predictor variables for training and testing the models. We assume that several predictor variables will lead to misclassification and incorrectness, especially soil parameters. Hence, the misclassification of these particular predictors should be avoided, using the strategy of tree boosting. The investigated area is the hydrological basin of Vernazza in Cinque Terre, Northwest Italy. A disastrous flash flood on the 25th of October 2011 with numerous landslides caused fatalities and economic losses amounting to millions of Euros. We mapped landslide areas in the field and checked the resulting maps with high resolution remote sensing images. Furthermore, the relevant soil parameters were collected based on a geostatistical approach. We measured topographic parameters, and physical and hydrological soil characteristics such as maximum shear strength under saturated and unsaturated conditions, and hydraulic conductivity (Ksat), and attributed random points in three distinguished classes: i) initiation areas, representing the most likely failure areas for possible landslides, ii) transport areas which were considered as a mix of classes 1 and 3, and iii) stable areas, such as valley bottom, ridges, and unconditionally stable areas. We ran both models with a training dataset (0.8 of the total points Ntot) and a test dataset (0.2 of Ntot) and each with 2000 grown decision trees. We validated the models with a Receiver Operating Characteristic (ROC) curve integral. The regionalized results of the TreeNet dataset yielded potential susceptible landslide areas of a total area of 1.74 km², which is 29.74% of the total area. In contrast, the Random Forests model classified a much greater susceptible area (84.27% of the total area). The results show that Treenet is outperforming RF. The latter misclassifies especially the soil related variables, whereas TreeNet yields robust model results. Published Online: 2016/02/03 11:48:22 Object Identifier: 0xc1aa5576 0x0032b57d Rights: .
The Journal for Geographic Information Science issue 1-2015 presents peer-reviewed papers
presented at the Geoinformatics
Forum (www.gi-forum.org), held in Salzburg from July 7-10,
2015. The annual GI_Forum symposium provides a platform for dialogue among geospatial minds
in an ongoing effort to support the creation of an informed GISociety.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |