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

Binh Thai Pham, Indra Prakash, Wei Chen, Hai-Bang Ly, Lanh Si Ho, Ebrahim Omidvar, Tran Van Phong and Dieu Tien Bui
Additional contact information
Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam
Indra Prakash: Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
Wei Chen: College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
Hai-Bang Ly: University of Transport Technology, Hanoi 100000, Vietnam
Lanh Si Ho: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Ebrahim Omidvar: Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran
Tran Van Phong: Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, Hanoi 10000, Vietnam
Dieu Tien Bui: Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Bø i Telemark N-3800, Norway

Sustainability, 2019, vol. 11, issue 22, 1-30

Abstract: 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.

Keywords: landslides; GIS; sequential minimal optimization; support vector machines; Viet Nam (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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