Improvement Of Landslide Prediction System Based On Hybrid Neural Networks (Penang Island, Malaysia)
Landslides are one of the most aggressive natural disasters that cause loss of lives and of billions dollars in damages annually worldwide. They pose a threat to the safety of human lives, the environment, resources and property. It is one of the natural disasters that occur quite often in Malays...
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| Format: | Thesis |
| Language: | English |
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2015
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| Online Access: | http://eprints.usm.my/46579/ http://eprints.usm.my/46579/1/Pages%20from%20Improvement%20Of%20Landslide%20Prediction%20System%20Based%20On%20Hybrid%20Neural%20Networks%20%28Penang%20Island%2C%20Malaysia%29%2024%20PAGE.pdf |
| _version_ | 1848880644799594496 |
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| author | Shabib Hussien Alkhasawneh, Mutasem |
| author_facet | Shabib Hussien Alkhasawneh, Mutasem |
| author_sort | Shabib Hussien Alkhasawneh, Mutasem |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Landslides are one of the most aggressive natural disasters that cause loss of
lives and of billions dollars in damages annually worldwide. They pose a threat to the
safety of human lives, the environment, resources and property. It is one of the
natural disasters that occur quite often in Malaysia and particularly in Penang Island
during heavy rainy seasons. Numerous researches on landslides studies have been
done based on Penang Island. However, many issues seriously related to landslides
have not been solved yet. These issues include the extraction of new factors which
cause landslides, investigation on the optimum factors which cause landslides and
the generation of an accurate landslide hazard map for Penang island. In addition to
that, the landslide hazard prediction intelligent system, either for Penang Island or for
the entire world is still being investigated up to this date. For that reason, an
intelligent landslide hazard mapping system is proposed. It consists of three stages:
factor extraction, factor selection and Artificial Neural Network (ANNs) as an
analysis tool. Twenty one factors are used in this study where nine factors were
collected from different governmental agents. The rest of the factors (twelve) were
extracted from the Digital Elevation Models (DEM), seven of these factors were
extracted and used for the first time on Penang Island. In the factor selection phase.
six factor selection techniques are employed to select the most important factors in
the landslide prediction. |
| first_indexed | 2025-11-15T18:06:24Z |
| format | Thesis |
| id | usm-46579 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:06:24Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-465792020-06-19T02:05:03Z http://eprints.usm.my/46579/ Improvement Of Landslide Prediction System Based On Hybrid Neural Networks (Penang Island, Malaysia) Shabib Hussien Alkhasawneh, Mutasem TK1-9971 Electrical engineering. Electronics. Nuclear engineering Landslides are one of the most aggressive natural disasters that cause loss of lives and of billions dollars in damages annually worldwide. They pose a threat to the safety of human lives, the environment, resources and property. It is one of the natural disasters that occur quite often in Malaysia and particularly in Penang Island during heavy rainy seasons. Numerous researches on landslides studies have been done based on Penang Island. However, many issues seriously related to landslides have not been solved yet. These issues include the extraction of new factors which cause landslides, investigation on the optimum factors which cause landslides and the generation of an accurate landslide hazard map for Penang island. In addition to that, the landslide hazard prediction intelligent system, either for Penang Island or for the entire world is still being investigated up to this date. For that reason, an intelligent landslide hazard mapping system is proposed. It consists of three stages: factor extraction, factor selection and Artificial Neural Network (ANNs) as an analysis tool. Twenty one factors are used in this study where nine factors were collected from different governmental agents. The rest of the factors (twelve) were extracted from the Digital Elevation Models (DEM), seven of these factors were extracted and used for the first time on Penang Island. In the factor selection phase. six factor selection techniques are employed to select the most important factors in the landslide prediction. 2015-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46579/1/Pages%20from%20Improvement%20Of%20Landslide%20Prediction%20System%20Based%20On%20Hybrid%20Neural%20Networks%20%28Penang%20Island%2C%20Malaysia%29%2024%20PAGE.pdf Shabib Hussien Alkhasawneh, Mutasem (2015) Improvement Of Landslide Prediction System Based On Hybrid Neural Networks (Penang Island, Malaysia). PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | TK1-9971 Electrical engineering. Electronics. Nuclear engineering Shabib Hussien Alkhasawneh, Mutasem Improvement Of Landslide Prediction System Based On Hybrid Neural Networks (Penang Island, Malaysia) |
| title | Improvement Of Landslide Prediction System Based On
Hybrid Neural Networks (Penang Island, Malaysia) |
| title_full | Improvement Of Landslide Prediction System Based On
Hybrid Neural Networks (Penang Island, Malaysia) |
| title_fullStr | Improvement Of Landslide Prediction System Based On
Hybrid Neural Networks (Penang Island, Malaysia) |
| title_full_unstemmed | Improvement Of Landslide Prediction System Based On
Hybrid Neural Networks (Penang Island, Malaysia) |
| title_short | Improvement Of Landslide Prediction System Based On
Hybrid Neural Networks (Penang Island, Malaysia) |
| title_sort | improvement of landslide prediction system based on
hybrid neural networks (penang island, malaysia) |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/46579/ http://eprints.usm.my/46579/1/Pages%20from%20Improvement%20Of%20Landslide%20Prediction%20System%20Based%20On%20Hybrid%20Neural%20Networks%20%28Penang%20Island%2C%20Malaysia%29%2024%20PAGE.pdf |