A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility

In this paper, the main objective is to discover an application of a novel classifier based on Composite Hyper-cubes on Iterated Random Projections (CHIRP) for assessment of landslide susceptibility at the Uttarakhand Area (India). For this, 1295 historical landslides events and landslide affecting parameters were collected and used for creating training and testing datasets. Other benchmark models namely Logistic Regression (LR), RBF neural network (ANN-RBF), and Naive Bayes (NB) were chosen for comparison. Analysis results indicate that the CHIRP is the best, followed by the LR, the ANN-RBF, and the NB, respectively. Overall, the CHIRP indicates as a promising and good alternative method that could be used to assess landslide susceptibility in other landslide prone areas.