Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets

This study aimed to examine the influence of the random selection of landslide training and testing sets on the predictive performance of the shallow landslide susceptibility modelling at regional scale. The performance of frequency ratio (FR), information value (IV), logistic regression (LR), and maximum entropy (ME) methods were tested and compared for modeling shallow landslide susceptibility in Calabria region, (South Italy). A landslide database of 22,028 shallow landslides, randomly split into training (70%) and testing (30%) sets, was combined with 15 predisposing factors (lithology, soil texture, soil bulk density, soil erodibility, drainage density, land use, elevation, local relief, slope gradient, slope aspect, plan curvature, topographic wetness index, stream power index, topographic ruggedness index, and topographic position index) to calibrate and validate the models. The robustness of the models in response to changes in the landslide dataset was explored through ten training and test sets replicates. The performance of these models was evaluated using several statistical indices and the ROC curve method. The results showed that all the four methods applied achieve promising performance on the prediction of shallow landslide susceptibility at regional scale. The comparison between four methods displayed that the ME is the best performing (AUC = 0.866), followed by the LR (AUC = 0.845), FR (AUC = 0.813), and finally IV (AUC = 0.800). In addition, the findings showed that the accuracy of the four methods for modeling shallow landslide susceptibility was quite robust when the training and testing sets were changed (i.e. a very low sensitivity to varying training/testing sets).