Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Ma...

Full description

Saved in:
Bibliographic Details
Main Authors: Pradhan, Biswajeet, Abokharima, Mohammed Hasan, Jebur, Mustafa Neamah, Tehrany, Mahyat Shafapour
Format: Article
Published: Springer 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34347/
http://link.springer.com/article/10.1007%2Fs11069-014-1128-1
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.34347
record_format eprints
spelling my.upm.eprints.343472015-12-10T06:59:31Z http://psasir.upm.edu.my/id/eprint/34347/ Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS Pradhan, Biswajeet Abokharima, Mohammed Hasan Jebur, Mustafa Neamah Tehrany, Mahyat Shafapour Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses. Springer 2014 Article PeerReviewed Pradhan, Biswajeet and Abokharima, Mohammed Hasan and Jebur, Mustafa Neamah and Tehrany, Mahyat Shafapour (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards, 73 (2). pp. 1019-1042. ISSN 0921-030X; ESSN: 1573-0840 http://link.springer.com/article/10.1007%2Fs11069-014-1128-1 10.1007/s11069-014-1128-1
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.
format Article
author Pradhan, Biswajeet
Abokharima, Mohammed Hasan
Jebur, Mustafa Neamah
Tehrany, Mahyat Shafapour
spellingShingle Pradhan, Biswajeet
Abokharima, Mohammed Hasan
Jebur, Mustafa Neamah
Tehrany, Mahyat Shafapour
Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
author_facet Pradhan, Biswajeet
Abokharima, Mohammed Hasan
Jebur, Mustafa Neamah
Tehrany, Mahyat Shafapour
author_sort Pradhan, Biswajeet
title Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
title_short Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
title_full Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
title_fullStr Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
title_full_unstemmed Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
title_sort land subsidence susceptibility mapping at kinta valley (malaysia) using the evidential belief function model in gis
publisher Springer
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34347/
http://link.springer.com/article/10.1007%2Fs11069-014-1128-1
_version_ 1643831126466232320
score 13.209306