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Open AccessFeature PaperArticle

Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms

1
Department of Geography, Social Science Centre, Western University, London, ON N6A 5C2, Canada
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Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71441-65186, Iran
3
Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz 71467-13565, Iran
4
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Water 2019, 11(11), 2292; https://doi.org/10.3390/w11112292
Received: 23 August 2019 / Revised: 22 October 2019 / Accepted: 24 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)
The aim of this study was to apply data mining algorithms to produce a landslide susceptibility map of the national-scale catchment called Bandar Torkaman in northern Iran. As it was impossible to directly use the advanced data mining methods due to the volume of data at this scale, an intermediate approach, called normalized frequency-ratio unique condition units (NFUC), was devised to reduce the data volume. With the aid of this technique, different data mining algorithms such as fuzzy gamma (FG), binary logistic regression (BLR), backpropagation artificial neural network (BPANN), support vector machine (SVM), and C5 decision tree (C5DT) were employed. The success and prediction rates of the models, which were calculated by receiver operating characteristic curve, were 0.859 and 0.842 for FG, 0.887 and 0.855 for BLR, 0.893 and 0.856 for C5DT, 0.891 and 0.875 for SVM, and 0.896 and 0.872 for BPANN that showed the highest validation rates as compared with the other methods. The proposed approach of NFUC proved highly efficient in data volume reduction, and therefore the application of computationally demanding algorithms for large areas with voluminous data was feasible. View Full-Text
Keywords: landslide susceptibility; data mining methods; small scale; normalized frequency ratio; unique condition unites; spatial modeling; geographic information system landslide susceptibility; data mining methods; small scale; normalized frequency ratio; unique condition unites; spatial modeling; geographic information system
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Vakhshoori, V.; Pourghasemi, H.R.; Zare, M.; Blaschke, T. Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms. Water 2019, 11, 2292.

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