Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia
Increasing pressure for development in Malaysia in recent years due to rapid population growth and urbanization has caused numerous environmental related problems such as landslide and soil erosion. Increasing landslide event in Malaysia has caused degradation to properties, life and environment. Th...
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Environmental Management Society, Malaysia
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ukm-22002016-12-14T06:30:57Z http://journalarticle.ukm.my/2200/ Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia Wan Mohd Muhiyuddin Wan Ibrahim, Ruslan Rainis, Increasing pressure for development in Malaysia in recent years due to rapid population growth and urbanization has caused numerous environmental related problems such as landslide and soil erosion. Increasing landslide event in Malaysia has caused degradation to properties, life and environment. This paper describes a study to develop landslide model by using logistic regression approach, partly to measure the significance of each causative factor that contributes to landslide. In this study causative factors are divided into physical, human activities and location. This model is based upon the model developed by a number of researchers. Landslide events in the Hulu Sungai Langat sub-basin, which is the upper stream of Langat Basin, Selangor, Malaysia were used to develop the model Environmental Management Society, Malaysia 2004 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/2200/1/2004_7_RuslanRainis.pdf Wan Mohd Muhiyuddin Wan Ibrahim, and Ruslan Rainis, (2004) Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia. Malaysian Journal of Environmental Management, 5 . pp. 113-122. ISSN 1511-7855 http://www.ems-malaysia.org/mjem/index.html |
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Digital Repository |
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Universiti Kebangasaan Malaysia |
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UKM Institutional Repository |
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Online Access |
language |
English |
description |
Increasing pressure for development in Malaysia in recent years due to rapid population growth and urbanization has caused numerous environmental related problems such as landslide and soil erosion. Increasing landslide event in Malaysia has caused degradation to properties, life and environment. This paper describes a study to develop landslide model by using logistic regression approach, partly to measure the significance of each causative factor that contributes to landslide. In this study causative factors are divided into physical, human activities and location. This model is based upon the model developed by a number of researchers. Landslide events in the Hulu Sungai Langat sub-basin, which is the upper stream of Langat Basin, Selangor, Malaysia were used to develop the model |
format |
Article |
author |
Wan Mohd Muhiyuddin Wan Ibrahim, Ruslan Rainis, |
spellingShingle |
Wan Mohd Muhiyuddin Wan Ibrahim, Ruslan Rainis, Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
author_facet |
Wan Mohd Muhiyuddin Wan Ibrahim, Ruslan Rainis, |
author_sort |
Wan Mohd Muhiyuddin Wan Ibrahim, |
title |
Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
title_short |
Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
title_full |
Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
title_fullStr |
Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
title_full_unstemmed |
Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia |
title_sort |
modelling landslide using gis and rs: a case study of upper stream of langat river basin, malaysia |
publisher |
Environmental Management Society, Malaysia |
publishDate |
2004 |
url |
http://journalarticle.ukm.my/2200/ http://journalarticle.ukm.my/2200/ http://journalarticle.ukm.my/2200/1/2004_7_RuslanRainis.pdf |
first_indexed |
2023-09-18T19:35:27Z |
last_indexed |
2023-09-18T19:35:27Z |
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1777405240352440320 |