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

Quang-Khanh Nguyen, Dieu Tien Bui, Nhat-Duc Hoang, Phan Trong Trinh, Viet-Ha Nguyen and Isık Yilmaz
Additional contact information
Quang-Khanh Nguyen: Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
Dieu Tien Bui: Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, Bø i Telemark N-3800, Norway
Nhat-Duc Hoang: Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-K7/25 Quang Trung, Danang 556361, Vietnam
Phan Trong Trinh: Institute of Geological Sciences, Vietnam Academy of Sciences and Technology (VASC), 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
Viet-Ha Nguyen: Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
Isık Yilmaz: Department of Geological Engineering, Faculty of Engineering, Cumhuriyet University, Sivas 58140, Turkey

Sustainability, 2017, vol. 9, issue 5, 1-24

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.

Keywords: landslide; classifier ensemble; instance based learning; Rotation Forest; GIS; Vietnam (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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