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タイトル: | Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree |
著者: | Truong, Xuan Mitamura, Muneki Kono, Yasuyuki Raghavan, Venkatesh Yonezawa, Go Truong, Xuan Do, Thi Tien Bui, Dieu Lee, Saro |
著者名の別形: | 河野, 泰之 |
キーワード: | landslide bagging ensemble Logistic Model Trees GIS Vietnam |
発行日: | Jul-2018 |
出版者: | MDPI AG |
誌名: | Applied Sciences |
巻: | 8 |
号: | 7 |
論文番号: | 1046 |
抄録: | 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. |
著作権等: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
URI: | http://hdl.handle.net/2433/232949 |
DOI(出版社版): | 10.3390/app8071046 |
出現コレクション: | 学術雑誌掲載論文等 |
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