Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques

The quality of digital elevation models (DEMs), as well as their spatial resolution, are important issues in geomorphic studies. However, their influence on landslide susceptibility mapping (LSM) remains poorly constrained. This work determined the scale dependency of DEM-derived geomorphometric factors in LSM using a 5 m LiDAR DEM, LiDAR resampled 30 m DEM, and a 30 m ASTER DEM. To verify the validity of our approach, we first compiled an inventory map comprising of 267 landslides for Sihjhong watershed, Taiwan, from 2004 to 2014. Twelve landslide causative factors were then generated from the DEMs and ancillary data. Afterward, popular statistical and machine learning techniques, namely, logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented to produce the LSM. The accuracies of models were evaluated by overall accuracy, kappa index and the receiver operating characteristic curve indicators. The highest accuracy was attained from the resampled 30 m LiDAR DEM derivatives, indicating a fine-resolution topographic data does not necessarily achieve the best performance. Additionally, RF attained superior performance between the three presented models. Our findings could contribute to opt for an appropriate DEM resolution for mapping landslide hazard in vulnerable areas.