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A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment

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Abstract

Landslide susceptibility assessment was performed using the novel hybrid model Bagging-based Naïve Bayes Trees (BAGNBT) at Mu Cang Chai district, located in northern Viet Nam. The model was validated using the Chi-square test, statistical indexes, and area under the receiver operating characteristic curve (AUC). In addition, other models, namely the Rotation Forest-based Naïve Bayes Trees (RFNBT), single Naïve Bayes Trees (NBT), and Support Vector Machines (SVM), were selected for the comparison. Results show that the novel hybrid model (AUC = 0.834) outperformed the RFNBT (0.830), SVM (0.805), and NBT (0.800). This indicates that the BAGNBT is a promising and better alternative method for landslide susceptibility modeling and mapping.

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Acknowledgements

The authors express their sincere thanks to the Vietnam Institute of Geosciences and Mineral Resources for providing the data and to the Director of BISAG, Gujarat, India for providing facilities for this research work.

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Authors and Affiliations

  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam

    Binh Thai Pham

  2. Department of Science & Technology, Government of Gujarat, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar, India

    Indra Prakash

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  1. Binh Thai Pham
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  2. Indra Prakash
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Correspondence to Binh Thai Pham.

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Pham, B.T., Prakash, I. A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment. Bull Eng Geol Environ 78, 1911–1925 (2019). https://doi.org/10.1007/s10064-017-1202-5

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  • DOI: https://doi.org/10.1007/s10064-017-1202-5

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