A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam

Abstract

The main objective of this study is to investigate potential application of the Fuzzy Unordered Rules Induction Algorithm (FURIA) and the Bagging (an ensemble technique) in comparison with Decision Tree model for spatial prediction of shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then, the landslide inventory was randomly partitioned into 70 % for training the models and 30 % for the model validation. Second, six landslide conditioning factors (slope, aspect, lithology, land use, soil type, and distance to faults) were prepared. Using these factors and the training dataset, landslide susceptibility indexes were calculated using the FURIA, the FURIA with Bagging, the Decision Tree, and the Decision Tree with Bagging. Finally, prediction performances of these susceptibility maps were carried out using the Receiver Operating Characteristic (ROC) technique. The results show that area under the ROC curve (AUC) using training dataset has the largest for the Decision Tree with Bagging (0.925) and the FURIA with Bagging (0.913), followed by the Decision Tree (0.908) and the FURIA (0.878). The prediction capability of these models was estimated using the validation dataset. The highest prediction was achieved using the FURIA with Bagging (AUC = 0.802), followed by the Decision Tree (AUC = 0.783), the Decision Tree with Bagging (AUC = 0.777), and the FURIA (AUC = 0.773). We conclude that the FURIA with Bagging is the best model in this study.