Landslide Susceptibility Assessment by Applying the Coupling Method of Radial Basis Neural Network and Adaboost: A Case Study from the Three Gorges Reservoir Area

Accurate landslide susceptibility map is an important basis for landslide risk assessment. In order to improve the accuracy of landslide susceptibility assessment,Longjuba area in the Three Gorges reservoir area was taken as a case study,10 factors (i.e. slope) were selected and parepared,and the frequency ratio method was used to analyze the relationship between each factor and landslide development quantitatively. 70% landslides were randomly selected as training samples while the 30% were adopted for testing,the radial basis neural network and adaboost ensemble learning coupled model (RBNN-Adaboost),radial basis neural network (RBNN) and logistic regression (LR) model were used to make the assessment of landslide susceptibility,respectively. Results show that factors of distance to river,slope and so on are the main controlling factors of landslide development; RBNN-Adaboost shows the best prediction accuracy (0.820) than logistic regression model (0.748) and RBNN (0.781). The adaboost ensemble learning can further improve the prediction performance of the model. By combining the advantages of RBNN and adaboost,the proposed method achieves the highest prediction accuracy,which is a reliable assessment model of landslide susceptibility.