Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps

In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study a...

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Main Authors:	Simon, Norbert, De Roiste, Mairead, Crozier, Michael, Abdul Ghani Rafek
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2017
Online Access:	http://journalarticle.ukm.my/10589/ http://journalarticle.ukm.my/10589/ http://journalarticle.ukm.my/10589/1/04%20Norbert%20Simon.pdf

id	ukm-10589
recordtype	eprints
spelling	ukm-105892017-08-22T06:56:29Z http://journalarticle.ukm.my/10589/ Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps Simon, Norbert De Roiste, Mairead Crozier, Michael Abdul Ghani Rafek, In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon) applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets both representing the same landslides within the study area. These datasets were later separated into training and validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will influence the accuracy of the final landslide potential map. Penerbit Universiti Kebangsaan Malaysia 2017-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/10589/1/04%20Norbert%20Simon.pdf Simon, Norbert and De Roiste, Mairead and Crozier, Michael and Abdul Ghani Rafek, (2017) Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps. Sains Malaysiana, 46 (1). pp. 27-34. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol46num1_2017/contentsVol46num1_2017.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon) applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets both representing the same landslides within the study area. These datasets were later separated into training and validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will influence the accuracy of the final landslide potential map.
format	Article
author	Simon, Norbert De Roiste, Mairead Crozier, Michael Abdul Ghani Rafek,
spellingShingle	Simon, Norbert De Roiste, Mairead Crozier, Michael Abdul Ghani Rafek, Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
author_facet	Simon, Norbert De Roiste, Mairead Crozier, Michael Abdul Ghani Rafek,
author_sort	Simon, Norbert
title	Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
title_short	Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
title_full	Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
title_fullStr	Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
title_full_unstemmed	Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps
title_sort	representing landslides as polygon (areal) or points? how different data types influence the accuracy of landslide susceptibility maps
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2017
url	http://journalarticle.ukm.my/10589/ http://journalarticle.ukm.my/10589/ http://journalarticle.ukm.my/10589/1/04%20Norbert%20Simon.pdf
first_indexed	2023-09-18T19:57:53Z
last_indexed	2023-09-18T19:57:53Z
_version_	1777406652132098048

Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps

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