Conditioning factors determination for landslide susceptibility mapping using support vector machine learning
This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each datas...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/78128/1/Conditioning%20factors%20determination%20for%20landslide%20susceptibility%20mapping%20using%20support%20vector%20machine%20learning.pdf |
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| author | Kalantar, Bahareh Ueda, Naonori Lay, Usman Salihu Al-Najjar, Husam Abdulrasool H. Abdul Halin, Alfian |
| author_facet | Kalantar, Bahareh Ueda, Naonori Lay, Usman Salihu Al-Najjar, Husam Abdulrasool H. Abdul Halin, Alfian |
| author_sort | Kalantar, Bahareh |
| collection | UPM |
| description | This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each dataset (SVM-G1 and SVM-G2) in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 160 landslide inventory datasets of the study area were used where 70% was used for SVM training and 30% for testing. The intra-relationships between parameters were explored based on variance inflation factors (VIF), Pearson's correlation and Cohen's kappa analysis. Other evaluation metrics are the area under curve (AUC). |
| first_indexed | 2024-03-06T10:22:49Z |
| format | Conference or Workshop Item |
| id | upm.eprints-78128 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:22:49Z |
| publishDate | 2019 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-781282020-06-15T01:51:53Z http://psasir.upm.edu.my/id/eprint/78128/ Conditioning factors determination for landslide susceptibility mapping using support vector machine learning Kalantar, Bahareh Ueda, Naonori Lay, Usman Salihu Al-Najjar, Husam Abdulrasool H. Abdul Halin, Alfian This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each dataset (SVM-G1 and SVM-G2) in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 160 landslide inventory datasets of the study area were used where 70% was used for SVM training and 30% for testing. The intra-relationships between parameters were explored based on variance inflation factors (VIF), Pearson's correlation and Cohen's kappa analysis. Other evaluation metrics are the area under curve (AUC). IEEE 2019 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/78128/1/Conditioning%20factors%20determination%20for%20landslide%20susceptibility%20mapping%20using%20support%20vector%20machine%20learning.pdf Kalantar, Bahareh and Ueda, Naonori and Lay, Usman Salihu and Al-Najjar, Husam Abdulrasool H. and Abdul Halin, Alfian (2019) Conditioning factors determination for landslide susceptibility mapping using support vector machine learning. In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 28 July-2 Aug. 2019, Yokohama, Japan. (pp. 9626-9629). 10.1109/IGARSS.2019.8898340 |
| spellingShingle | Kalantar, Bahareh Ueda, Naonori Lay, Usman Salihu Al-Najjar, Husam Abdulrasool H. Abdul Halin, Alfian Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title | Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title_full | Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title_fullStr | Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title_full_unstemmed | Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title_short | Conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| title_sort | conditioning factors determination for landslide susceptibility mapping using support vector machine learning |
| url | http://psasir.upm.edu.my/id/eprint/78128/1/Conditioning%20factors%20determination%20for%20landslide%20susceptibility%20mapping%20using%20support%20vector%20machine%20learning.pdf |
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