Journal
ENGINEERING GEOLOGY
Volume 123, Issue 3, Pages 225-234Publisher
ELSEVIER
DOI: 10.1016/j.enggeo.2011.09.006
Keywords
Landslide susceptibility; Support Vector Machines; Decision Tree; Logistic Regression; Analytical Hierarchy Process; Classification
Funding
- Czech Science Foundation [205/09/079]
- Ministry of Science of the Republic of Serbia [III 47014]
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This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the classification rules. Three different machine learning algorithms were compared: Support Vector Machines, Decision Trees and Logistic Regression. A specific area of the Fruska Gora Mountain (Serbia) was selected to perform the entire modeling procedure, from attribute and referent data preparation/processing, through the classifiers' implementation to the evaluation, carried out in terms of the model's performance and agreement with the referent data. The experiments showed that Support Vector Machines outperformed the other proposed methods, and hence this algorithm was selected as the model of choice to be compared with a common knowledge-driven method - the Analytical Hierarchy Process - to create a landslide susceptibility map of the relevant area. The SVM classifier outperformed the AHP approach in all evaluation metrics (kappa index, area under ROC curve and false positive rate in stable ground class). (C) 2011 Elsevier B.V. All rights reserved.
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