The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen commune (Tetouan, Northern Morocco)

Abstract

Landslide susceptibility maps (LSMs) rely on statistical association for weighting the impact of each predictive factor class. However, the effects of using different training datasets and causative factors combinations on the accuracy of the results are rarely discussed. Accordingly, we investigated the effects of training data and computation technique selection on the accuracy and performance of LSMs. Results show that including relict landslides in the training data diminishes the accuracy of the LSMs compared to models that exclude such processes from the input. As for the effect of choosing different combinations of predictive variables, it was shown to be less significant. After investigating association and causality between predictive variables, the best performing models produced using frequency ratio and logistic regression tend to be more accurate than those including all variables. As for the artificial neural networks, it seems to outperform other models. Indeed, such investigations are beneficial for improving LSMs.