Abstract
The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower.
Highlights
Vietnam is identified as a country that is vulnerable to some of the worst manifestations of climate change such as sea level rise, flooding, and landslides
The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines Support Vector Machines (SVM), decision tree Decision Tree (DT), and Naıve Bayes NB models for spatial prediction of landslide hazards in the Hoa Binh province Vietnam
The main objective of this study is to investigate and compare the results of three data mining approaches, that is, SVM, DT, and NB, to spatial prediction of landslide hazards for the Hoa Binh province Vietnam
Summary
Vietnam is identified as a country that is vulnerable to some of the worst manifestations of climate change such as sea level rise, flooding, and landslides. In the case of SVM, the main advantage of this method is that it can use large input data with fast learning capacity This method is well-suited to nonlinear high-dimensional data modeling problems and provides promising perspectives in the landslide susceptibility mapping. The main objective of this study is to investigate and compare the results of three data mining approaches, that is, SVM, DT, and NB, to spatial prediction of landslide hazards for the Hoa Binh province Vietnam. The main difference between this study and the aforementioned works is that SVM with two kernel functions radial basis and polynomial kernels and NB were applied for landslide susceptibility modeling To assess these methods, the susceptibility maps obtained from the three data mining approaches were compared to those obtained by the logistic regression model reported by the same authors 2. The computation process was carried out using MATLAB 7.11 and LIBSVM 50 for SVM and WEKA ver. 3.6.6 The University of Waikato, 2011 for DT and NB
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