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

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Quang-Khanh Nguyen Q. N., Dieu Tien Bui D. T. B., Nhat-Duc Hoang N. H., Phan Trong Trinh P. T. T., Viet-Ha Nguyen V. N., YILMAZ I.

SUSTAINABILITY, cilt.9, sa.5, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 5
  • Basım Tarihi: 2017
  • Doi Numarası: 10.3390/su9050813
  • Dergi Adı: SUSTAINABILITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Anahtar Kelimeler: landslide, classifier ensemble, instance based learning, Rotation Forest, GIS, Vietnam, ARTIFICIAL NEURAL-NETWORKS, SUPPORT VECTOR MACHINE, LOGISTIC-REGRESSION, SUSCEPTIBILITY ASSESSMENT, DECISION TREE, HONG-KONG, MODELS, AREA, HAZARD, MULTIVARIATE
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.