Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp

This study aimed to produce a high-quality landslide susceptibility map for Teziutlan municipality, a landslide-prone region in Mexico, which is characterised by a depositional pyroclastic ramp. The heterogeneous quality of available topographic information (i.e. higher resolution digital elevation model only for a sub-region) encouraged to confront modelling results based on two different study area delineations and two raster resolutions. Input data was based on the larger modelling region L15 (163 km(2)) and smaller S (70 km(2); located inside L15) with an associated raster cell size of 15 m (region L15 and S15) and 5 m (region S5). The resulting three data sets (L15, S15 and S5) were included into three differently flexible modelling techniques (Generalized Linear Model - GLM, General Additive Model - GAM, Support Vector Machine-SVM) to produce nine landslide susceptibility models. Preceding variable selection was performed heuristically and supported by an exploratory data analysis. The final models were based on the explanatory variables slope angle, slope aspect, lithology, relative slope position, elevation, convergence index, distance to streams, distance to springs and topographic wetness index. The ability of the models to classify independent test data was elaborated using a k-fold cross validation procedure and the AUROC (Area Under the Receiver Operating Characteristic) metric. In general, all produced landslide susceptibility maps depicted the hillslopes of the ravines, which cut the pyroclastic ramp, as prone to landsliding. The modelling results showed that predictive performances (i.e. AUROC values) slightly increased with an increasing flexibility of the applied modelling technique. Thus, SVM performed best, while the GAM outperformed the GLM. This tendency was most distinctive when modelling with the largest landslide sample size (i.e. data set L15; n = 662 landslides). Non-linear classifiers (GAMs, SVMs) performed slightly better when trained on the basis of lower raster resolution (data set S15) compared to the 5 m counterparts (data set S5). Highest predictive performance was obtained for the model based on data set L15 and the SVM classifier (median AUROC: 0.82). However, SVMs also indicated the highest degree of model overfitting. This study indicates that the decision to delineate a study area, the selection of a raster resolution as well as the chosen classification technique can affect varying aspects of subsequent modelling results. The results do not support the assumption that a higher raster resolution (i.e. a more detailed digital representation of the terrain) inevitably leads to better performing or geomorphically more plausible landslide susceptibility maps.