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Sustainability 2017, 9(5), 813; doi:10.3390/su9050813

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

1
Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
2
Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, Bø i Telemark N-3800, Norway
3
Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-K7/25 Quang Trung, Danang 556361, Vietnam
4
Institute of Geological Sciences, Vietnam Academy of Sciences and Technology (VASC), 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
5
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
6
Department of Geological Engineering, Faculty of Engineering, Cumhuriyet University, Sivas 58140, Turkey
*
Author to whom correspondence should be addressed.
Academic Editors: Yu-Pin Lin and Marc A. Rosen
Received: 14 January 2017 / Revised: 3 May 2017 / Accepted: 10 May 2017 / Published: 13 May 2017
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Abstract

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas. View Full-Text
Keywords: landslide; classifier ensemble; instance based learning; Rotation Forest; GIS; Vietnam landslide; classifier ensemble; instance based learning; Rotation Forest; 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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Nguyen, Q.-K.; Tien Bui, D.; Hoang, N.-D.; Trinh, P.T.; Nguyen, V.-H.; Yilmaz, I. A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS. Sustainability 2017, 9, 813.

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