Discriminant analysis as an efcient method for landslide susceptibility assessment in cities with the scarcity of predisposition data.

Abstract

The city of Ouro Preto, which is located in the state of Minas Gerais, Brazil, has a long history of mass movements infuenced by the regional geology, geomorphology, and

anthropic activities, which have resulted in harmful consequences to the population. How- ever, most of the studies conducted in the region are qualitative and are directly dependent

on the experience specialists. The aim of this study was to analyse the landslide suscepti- bility in the urban region of Ouro Preto quantitatively by using discriminant analysis. The

landslide inventory was obtained by using unmanned aerial vehicle images and feldwork.

ArcGIS 10.6 and R 3.5.1 software were used, and the following landslide predisposing fac- tors were considered: slope angle, slope aspect, profle curvature, and topographic wetness

index (TWI). As geological and geotechnical data are still scarce in the interior of Brazil, we only used data derived from topography to determine the efectiveness of these factors for analysing landslide susceptibility. The slope angle proved to be the factor that most diferentiated unstable from stable terrain, followed by TWI. The other parameters were not as efective in diferentiating the stability conditions. The model efciency was 88.6%, the specifcity was 93.3%, and the sensitivity was 85.0%. Also, the prediction and success curve were used to evaluate the accuracy of the proposed landslides model, by using the area under the curve (AUC) criteria. It was shown that the AUC values 0.851 for testing and 0.838 for training indicate that the developed model provides an excellent prediction. The main contribution of this work is the demonstration of the efectiveness of using easily accessible data (derived from topography) for analysing landslide susceptibility with amultivariate statistical method. This method can contribute valuable information to urban planning eforts in cities without the need for robust data.