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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881696/ https://www.ncbi.nlm.nih.gov/pubmed/24453846 http://dx.doi.org/10.1155/2013/415023 |
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author | Alkhasawneh, Mutasem Sh. Ngah, Umi Kalthum Tay, Lea Tien Mat Isa, Nor Ashidi Al-batah, Mohammad Subhi |
author_facet | Alkhasawneh, Mutasem Sh. Ngah, Umi Kalthum Tay, Lea Tien Mat Isa, Nor Ashidi Al-batah, Mohammad Subhi |
author_sort | Alkhasawneh, Mutasem Sh. |
collection | PubMed |
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 | Online Article Text |
id | pubmed-3881696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-38816962014-01-20 Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network Alkhasawneh, Mutasem Sh. Ngah, Umi Kalthum Tay, Lea Tien Mat Isa, Nor Ashidi Al-batah, Mohammad Subhi ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2013-12-18 /pmc/articles/PMC3881696/ /pubmed/24453846 http://dx.doi.org/10.1155/2013/415023 Text en Copyright © 2013 Mutasem Sh. Alkhasawneh et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article Alkhasawneh, Mutasem Sh. Ngah, Umi Kalthum Tay, Lea Tien Mat Isa, Nor Ashidi Al-batah, Mohammad Subhi Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881696/ https://www.ncbi.nlm.nih.gov/pubmed/24453846 http://dx.doi.org/10.1155/2013/415023 |
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