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 land...
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| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/415023 |
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| author | Mutasem Sh. Alkhasawneh Umi Kalthum Ngah Lea Tien Tay Nor Ashidi Mat Isa Mohammad Subhi Al-batah |
| author_facet | Mutasem Sh. Alkhasawneh Umi Kalthum Ngah Lea Tien Tay Nor Ashidi Mat Isa Mohammad Subhi Al-batah |
| author_sort | Mutasem Sh. Alkhasawneh |
| collection | DOAJ |
| description | 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. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors. |
| format | Article |
| id | doaj-art-17c398f81a1d4e4da2f32205c3f9284c |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-17c398f81a1d4e4da2f32205c3f9284c2025-02-03T01:01:16ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/415023415023Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP NetworkMutasem Sh. Alkhasawneh0Umi Kalthum Ngah1Lea Tien Tay2Nor Ashidi Mat Isa3Mohammad Subhi Al-batah4Imaging and Computational Intelligence (ICI) Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, MalaysiaImaging and Computational Intelligence (ICI) Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, MalaysiaImaging and Computational Intelligence (ICI) Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, MalaysiaImaging and Computational Intelligence (ICI) Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, MalaysiaDepartment of Computer Science, Faculty of Science and Information Technology, Jadara University, Irbid 21110, JordanLandslide 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. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.http://dx.doi.org/10.1155/2013/415023 |
| spellingShingle | Mutasem Sh. Alkhasawneh Umi Kalthum Ngah Lea Tien Tay Nor Ashidi Mat Isa Mohammad Subhi Al-batah Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network The Scientific World Journal |
| title | Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
| title_full | Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
| title_fullStr | Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
| title_full_unstemmed | Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
| title_short | Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
| title_sort | determination of important topographic factors for landslide mapping analysis using mlp network |
| url | http://dx.doi.org/10.1155/2013/415023 |
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