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Open AccessArticle

A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

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University of Transport Technology, Hanoi 100000, Vietnam
2
Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
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College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran
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Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, Hanoi 10000, Vietnam
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Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Bø i Telemark N-3800, Norway
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(22), 6323; https://doi.org/10.3390/su11226323
Received: 12 August 2019 / Revised: 21 September 2019 / Accepted: 24 September 2019 / Published: 11 November 2019
The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas. View Full-Text
Keywords: landslides; GIS; sequential minimal optimization; support vector machines; Viet Nam landslides; GIS; sequential minimal optimization; support vector machines; Viet Nam
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MDPI and ACS Style

Pham, B.T.; Prakash, I.; Chen, W.; Ly, H.-B.; Ho, L.S.; Omidvar, E.; Tran, V.P.; Bui, D.T. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability 2019, 11, 6323. https://doi.org/10.3390/su11226323

AMA Style

Pham BT, Prakash I, Chen W, Ly H-B, Ho LS, Omidvar E, Tran VP, Bui DT. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability. 2019; 11(22):6323. https://doi.org/10.3390/su11226323

Chicago/Turabian Style

Pham, Binh Thai; Prakash, Indra; Chen, Wei; Ly, Hai-Bang; Ho, Lanh Si; Omidvar, Ebrahim; Tran, Van Phong; Bui, Dieu Tien. 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping" Sustainability 11, no. 22: 6323. https://doi.org/10.3390/su11226323

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