Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms


  • Bahareh Ghasemian Department of Geomorphology, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran
  • Dawod Talebpoor Asl Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
  • Binh Thai Pham Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam
  • Mohammadtghi Avand Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
  • Huu Duy Nguyen Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Hanoi, Vietnam
  • Saeid Janizadeh Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran



Shallow landslide, machine learning, information gain ratio, classifier, GIS, Iran


Shallow landslides through land degrading not only lead to threat the properly and life of human but they also may produce huge ecosystem damages. The aim of this study was to compare the performance of two decision tree machine learning algorithms including classification and regression tree (CART) and reduced error pruning tree (REPTree) for shallow landslide susceptibility mapping in Bijar, Kurdistan province, Iran. We first used 20 conditioning factors and then they were tested by information gain ratio (IGR) technique to select the most important ones. We then constructed a geodatabase based on the selected factors along with a total of 111 landslide locations with a ratio of 80/20 (for calibration/validation). The performance of the models was checked by the true positive rate (TP Rate), false positive rate (FP Rate), precision, recall, F1-Measure, Kappa, mean absolute error, and area under the receiver operatic curve (AUC). Results of IGR specified that the slope angle and TWI had the most contribution to shallow landslide occurrence in the study area. Moreover, results concluded that although these models had a high goodness-of-fit and prediction accuracy, the CART model (AUC=0.856) outperformed the REPTree model (AUC=0.837). Therefore, the CART model can be used as a promising tool and also as a base classifier to hybrid with optimization algorithms and Meta classifiers for spatial prediction of shallow landslide-prone areas.


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How to Cite

Ghasemian, B., Asl, D. T., Pham, B. T., Avand, M., Nguyen, H. D., & Janizadeh, S. (2020). Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam Journal of Earth Sciences, 42(3), 208–227.

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