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A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam

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Advances and Applications in Geospatial Technology and Earth Resources (GTER 2017)

Abstract

In this study, we proposed a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility mapping at the Kon Tum Province, Viet Nam. Landslide affecting factors (slope angle, slope aspect, elevation, valley depth, land use, NDVI, soil type, lithology, distance to geology boundaries, and distance to faults), and 1404 past and current landslide locations have been first collected from the study area for generating training and testing datasets. Secondly, the hybrid model RFFT has been constructed for landslide susceptibility assessment using training dataset. Performance of the proposed RFFT model has been validated by analysis of the Receiver Operating Characteristic (ROC) curve and statistical indexes, and compared with a well-known landslide models namely Support Vector Machines (SVM) and the single FT. Results show that the proposed RFFT model has good performance for landslide susceptibility assessment. It has better predictive capability compared with well-known SVM model and single FT model. Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

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Acknowledgement

Authors are thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science & Technology, Government of Gujarat, Gandhinagar, Gujarat, India for providing facilities to carry out this research work.

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Authors and Affiliations

  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Viet Nam

    Binh Thai Pham

  2. Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, Viet Nam

    Viet-Tien Nguyen & Phan Trong Trinh

  3. Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam

    Van-Liem Ngo

  4. University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Huong Thanh Thi Ngo

  5. Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gulbringvegen 36, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

Authors
  1. Binh Thai Pham
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  2. Viet-Tien Nguyen
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  3. Van-Liem Ngo
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  4. Phan Trong Trinh
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  5. Huong Thanh Thi Ngo
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  6. Dieu Tien Bui
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Corresponding author

Correspondence to Binh Thai Pham .

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Editors and Affiliations

  1. GIS group, Department of Business and IT, University College of Southeast Norway , Bo i Telemark, Norway

    Dieu Tien Bui

  2. Department of Underground and Mining Construction, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Anh Ngoc Do

  3. Department of Exploration Geology, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Hoang-Bac Bui

  4. Faculty of Civil Engineering, Duy Tan University , Da Nang, Vietnam

    Nhat-Duc Hoang

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Pham, B.T., Nguyen, VT., Ngo, VL., Trinh, P.T., Ngo, H.T.T., Tien Bui, D. (2018). A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam. In: Tien Bui, D., Ngoc Do, A., Bui, HB., Hoang, ND. (eds) Advances and Applications in Geospatial Technology and Earth Resources. GTER 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-68240-2_12

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