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Appl. Sci. 2018, 8(7), 1046; https://doi.org/10.3390/app8071046

Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

1
Faculty of Information Technology, Hanoi University of Mining and Geology, No.14 Vien Street, Bac Tu Liem, Hanoi 10000, Vietnam
2
Graduate School for Creative Cities, Osaka City University, Osaka 558-8585, Japan
3
Center for Southeast Asian Studies, Kyoto University, Kyoto 606-8502, Japan
4
Faculty of Information Technology, Hanoi University of Natural Resources and Environment, No. 14 Phu Dien, Bac Tu Liem, Hanoi 10000, Vietnam
5
Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gulbringvegen 36, N-3800 Bø i Telemark, Norway
6
Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea
7
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 305-350, Korea
*
Authors to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 22 June 2018 / Accepted: 23 June 2018 / Published: 27 June 2018
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Abstract

The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas. View Full-Text
Keywords: landslide; bagging ensemble; Logistic Model Trees; GIS; Vietnam landslide; bagging ensemble; Logistic Model Trees; GIS; Vietnam
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Truong, X.L.; Mitamura, M.; Kono, Y.; Raghavan, V.; Yonezawa, G.; Truong, X.Q.; Do, T.H.; Tien Bui, D.; Lee, S. Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree. Appl. Sci. 2018, 8, 1046.

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