Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network
Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in la...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2013
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| Subjects: | |
| Online Access: | http://eprints.usm.my/38647/1/Determination_of_Important_Topographic_Factors_for_Landslide_Mapping_Analysis_Using_MLP_Network.pdf http://eprints.usm.my/38647/ http://dx.doi.org/10.1155/2013/415023 |
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| Summary: | Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect,
general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine
the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are
three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies
had identified mainly six topographic factors. Seven new additional factors have been proposed in this study.They are longitude
curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The
second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP)
network classification accuracy and Zhou’s algorithm. At the third stage, the factors with higher weights were used to improve
the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area,
longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature.Theclassification accuracy
of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors. |
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