Prediction of the landslide susceptibility: Which algorithm, which precision?

Abstract

Coupling machine learning algorithms with spatial analytical techniques for landslide susceptibility modeling is a worth considering issue. So, the current research intend to present the first comprehensive comparison among the performances of ten advanced machine learning techniques (MLTs) including artificial neural networks (ANNs), boosted regression tree (BRT), classification and regression trees (CART), generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS), naïve Bayes (NB), quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM) for modeling landslide susceptibility and evaluating the importance of variables in GIS and R open source software. This study was carried out in the Ghaemshahr Region, Iran. The performance of MLTs has been evaluated using the area under ROC curve (AUC-ROC) approach. The results showed that AUC values for ten MLTs vary from 62.4 to 83.7%. It has been found that the RF (AUC=83.7%) and BRT (AUC=80.7%) have the best performances comparison to other MLTs.