Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 3 May 2017 / Approved: 4 May 2017 / Online: 4 May 2017 (08:25:12 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sustainability 2017, 9, 813
DOI: 10.3390/su9050813

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.

Subject Areas

landslide; classifier ensemble; instance based learning; Rotation Forest; GIS; Vietnam

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