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

Author

Listed:
  • 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)

  • Van Phong Tran

    (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)

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.

Suggested Citation

  • Binh Thai Pham & Indra Prakash & Wei Chen & Hai-Bang Ly & Lanh Si Ho & Ebrahim Omidvar & Van Phong Tran & Dieu Tien Bui, 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 11(22), pages 1-30, November.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6323-:d:285804
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    References listed on IDEAS

    as
    1. Binh Thai Pham & Dieu Tien Bui & Indra Prakash & M. B. Dholakia, 2016. "Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 97-127, August.
    2. Biswajeet Pradhan & Mohammed Abokharima & Mustafa Jebur & Mahyat Tehrany, 2014. "Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 1019-1042, September.
    3. M. Ercanoglu & C. Gokceoglu & Th. Van Asch, 2004. "Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 32(1), pages 1-23, May.
    4. Ataollah Shirzadi & Lee Saro & Oh Hyun Joo & Kamran Chapi, 2012. "A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(2), pages 1639-1656, November.
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    1. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
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    3. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.

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