A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India)
Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of thes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/54823/1/A%20comparative%20study%20of%20different%20machine%20learning%20methods%20for%20landslide%20susceptibility%20assessment%20a%20case%20study%20of%20Uttarakhand%20area%20%28India%29.pdf |
| _version_ | 1796976150585540608 |
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| author | Pham, Binh Thai Pradhan, Biswajeet Bui, Dieu Tien Prakash, Indra Dholakia, M. B. |
| author_facet | Pham, Binh Thai Pradhan, Biswajeet Bui, Dieu Tien Prakash, Indra Dholakia, M. B. |
| author_sort | Pham, Binh Thai |
| collection | UPM |
| description | Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively. |
| first_indexed | 2024-03-06T09:21:40Z |
| format | Article |
| id | upm.eprints-54823 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:21:40Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-548232018-04-04T08:28:34Z http://psasir.upm.edu.my/id/eprint/54823/ A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) Pham, Binh Thai Pradhan, Biswajeet Bui, Dieu Tien Prakash, Indra Dholakia, M. B. Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively. Elsevier 2016-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54823/1/A%20comparative%20study%20of%20different%20machine%20learning%20methods%20for%20landslide%20susceptibility%20assessment%20a%20case%20study%20of%20Uttarakhand%20area%20%28India%29.pdf Pham, Binh Thai and Pradhan, Biswajeet and Bui, Dieu Tien and Prakash, Indra and Dholakia, M. B. (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environmental Modelling & Software, 84. pp. 240-250. ISSN 1364-8152 10.1016/j.envsoft.2016.07.005 |
| spellingShingle | Pham, Binh Thai Pradhan, Biswajeet Bui, Dieu Tien Prakash, Indra Dholakia, M. B. A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title | A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title_full | A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title_fullStr | A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title_full_unstemmed | A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title_short | A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India) |
| title_sort | comparative study of different machine learning methods for landslide susceptibility assessment a case study of uttarakhand area india |
| url | http://psasir.upm.edu.my/id/eprint/54823/1/A%20comparative%20study%20of%20different%20machine%20learning%20methods%20for%20landslide%20susceptibility%20assessment%20a%20case%20study%20of%20Uttarakhand%20area%20%28India%29.pdf |
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