Large scale landslide and flooding hazard susceptibility assessment using semi-automated frequency ratio (FR) model

As extreme weather events become more frequent and world's population is growing an increasing number of built areas and critical infrastructure networks are challenged by natural hazards like heavy rain, urban flooding or landslides. At the same time, the quantity and quality of remote sensing data delivering earth observation products is continuously increasing and widely accessible. With the help of high-resolution, open access data and fast engineering approaches new options arise to investigate objects at risk. This study presents a semi-automated fast engineering approach, deploying only open access tools and data to create a large-scale hazard susceptibility assessment. The model objectives include its ability to rapidly identify critical areas, which will further allow to derivate the exposure of critical infrastructures to hazards. A bivariate frequency ratio (FR) model is applied for flooding and landslide susceptibility mapping on two study sites within the German federal state of Bavaria. Flood and landslide conditioning factors are selected based on performance criterions. For improved comparability of the results different normalization approaches are used. The resulting hazard susceptibility maps are validated in both cases by hazard inventories and statistical analysis of the area under the receiver operator characteristics curve (AUROC). Further, the susceptibility is partitioned into five defined zones. The results lead to the following conclusions: (i) the model is able to produce overall sufficient predictive accuracy, (ii) a higher number of parameters does not necessarily lead to enhanced model performance, and (iii) a higher resolution of the digital elevation model (DEM) can significantly improve the predictive performance. Moreover, the automation is a large benefit regarding the preparation and validation of the model independently of the employed resolution.