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Appl. Sci. 2017, 7(7), 683; doi:10.3390/app7070683

Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea

1
Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
2
Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), Sejong-si 30147, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Saro Lee
Received: 8 June 2017 / Revised: 26 June 2017 / Accepted: 27 June 2017 / Published: 2 July 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
View Full-Text   |   Download PDF [8502 KB, uploaded 2 July 2017]   |  

Abstract

The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas. View Full-Text
Keywords: spatial data mining; SVM; ANN; validation; ROC spatial data mining; SVM; ANN; validation; ROC
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lee, S.; Lee, M.-J.; Jung, H.-S. Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea. Appl. Sci. 2017, 7, 683.

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