A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides
This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018
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| Online Access: | http://eprints.utm.my/86067/1/BaharinAhmad2018_ANovelIntegratedApproachofRelevanceVector.pdf |
| _version_ | 1796864168927690752 |
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| author | Dieu, Tien Bui Shahabi, Himan Shirzadi, Ataollah Chapi, Kamran Nhat, Duc Hoang Binh, Thai Pham Quang, Thanh Bui Chuyen, Trung Tran Panahi, Mahdi Ahmad, Baharin Saro, Lee |
| author_facet | Dieu, Tien Bui Shahabi, Himan Shirzadi, Ataollah Chapi, Kamran Nhat, Duc Hoang Binh, Thai Pham Quang, Thanh Bui Chuyen, Trung Tran Panahi, Mahdi Ahmad, Baharin Saro, Lee |
| author_sort | Dieu, Tien Bui |
| collection | ePrints |
| description | This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas. |
| first_indexed | 2024-03-05T20:37:50Z |
| format | Article |
| id | utm.eprints-86067 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:37:50Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-860672020-08-30T08:53:18Z http://eprints.utm.my/86067/ A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides Dieu, Tien Bui Shahabi, Himan Shirzadi, Ataollah Chapi, Kamran Nhat, Duc Hoang Binh, Thai Pham Quang, Thanh Bui Chuyen, Trung Tran Panahi, Mahdi Ahmad, Baharin Saro, Lee G Geography (General) GE Environmental Sciences This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas. MDPI AG 2018-10-01 Article PeerReviewed application/pdf en http://eprints.utm.my/86067/1/BaharinAhmad2018_ANovelIntegratedApproachofRelevanceVector.pdf Dieu, Tien Bui and Shahabi, Himan and Shirzadi, Ataollah and Chapi, Kamran and Nhat, Duc Hoang and Binh, Thai Pham and Quang, Thanh Bui and Chuyen, Trung Tran and Panahi, Mahdi and Ahmad, Baharin and Saro, Lee (2018) A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sensing, 10 (10). ISSN 2072-4292 http://dx.doi.org/10.3390/rs10101538 DOI:10.3390/rs10101538 |
| spellingShingle | G Geography (General) GE Environmental Sciences Dieu, Tien Bui Shahabi, Himan Shirzadi, Ataollah Chapi, Kamran Nhat, Duc Hoang Binh, Thai Pham Quang, Thanh Bui Chuyen, Trung Tran Panahi, Mahdi Ahmad, Baharin Saro, Lee A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title | A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title_full | A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title_fullStr | A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title_full_unstemmed | A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title_short | A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| title_sort | novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides |
| topic | G Geography (General) GE Environmental Sciences |
| url | http://eprints.utm.my/86067/1/BaharinAhmad2018_ANovelIntegratedApproachofRelevanceVector.pdf |
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