Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models
The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/40275/ http://psasir.upm.edu.my/id/eprint/40275/1/974638.pdf |
| _version_ | 1848849379040952320 |
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| author | Tien Bui, Dieu Pradhan, Biswajeet Lofman, Owe Revhaug, Inge |
| author_facet | Tien Bui, Dieu Pradhan, Biswajeet Lofman, Owe Revhaug, Inge |
| author_sort | Tien Bui, Dieu |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower. |
| first_indexed | 2025-11-15T09:49:27Z |
| format | Article |
| id | upm-40275 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:49:27Z |
| publishDate | 2012 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-402752015-09-15T06:48:32Z http://psasir.upm.edu.my/id/eprint/40275/ Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models Tien Bui, Dieu Pradhan, Biswajeet Lofman, Owe Revhaug, Inge The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower. Hindawi Publishing Corporation 2012 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/40275/1/974638.pdf Tien Bui, Dieu and Pradhan, Biswajeet and Lofman, Owe and Revhaug, Inge (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models. Mathematical Problems in Engineering, 2012. art. no. 974638. pp. 1-26. ISSN 1024-123X; ESSN: 1563-5147 http://www.hindawi.com/journals/mpe/2012/974638/ 10.1155/2012/974638 |
| spellingShingle | Tien Bui, Dieu Pradhan, Biswajeet Lofman, Owe Revhaug, Inge Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title | Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title_full | Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title_fullStr | Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title_full_unstemmed | Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title_short | Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and nave bayes models |
| title_sort | landslide susceptibility assessment in vietnam using support vector machines, decision tree, and nave bayes models |
| url | http://psasir.upm.edu.my/id/eprint/40275/ http://psasir.upm.edu.my/id/eprint/40275/ http://psasir.upm.edu.my/id/eprint/40275/ http://psasir.upm.edu.my/id/eprint/40275/1/974638.pdf |