Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms

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...

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Bibliographic Details
Published in:Water (Basel) Vol. 11; no. 11; p. 2292
Main Authors: Vakhshoori, Vali, Pourghasemi, Hamid Reza, Zare, Mohammad, Blaschke, Thomas
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2019
Subjects:
Algorithms
Analysis
China
Data mining
decision support systems
Geographic information systems
Germany
graphs
Information management
Iran
Landslides
Landslides & mudslides
Mapping
Neural networks
prediction
regression analysis
Remote sensing
River networks
Sediment transport
Support vector machines
Taiwan
United Kingdom
Watershed management
watersheds
United Kingdom
Iran
China
Germany
Taiwan
Caspian Sea
ISSN:2073-4441, 2073-4441
Online Access:Get full text
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Summary: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.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w11112292