A modular and scalable workflow for data-driven modelling of shallow landslide susceptibility

The spatial impact of a single shallow landslide is small compared to a deep-seated, impactful failure and hence its damage potential localized and limited. Yet, their higher frequency of occurrence and spatio-temporal correlation in response to external triggering events such as strong precipitation, nevertheless result in dramatic risks for population, infrastructure and environment. It is therefore essential to continuously investigate and analyze the spatial hazard that shallow landslides pose. Its visualisation through regularly-updated, dynamic hazard maps can be used by decision and policy makers. Even though a number of data-driven approaches for shallow landslide hazard mapping exist, a generic workflow has not yet been described. Therefore, we introduce a scalable and modular machine learning-based workflow for shallow landslide hazard prediction in this study. The scientific test case for the development of the workflow investigates the rainfall-triggered shallow landslide hazard in Switzerland. A benchmark dataset was compiled based on a historic landslide database as presence data, as well as a pseudo-random choice of absence locations, to train the data-driven model. Features included in this dataset comprise at the current stage 14 parameters from topography, soil type, land cover and hydrology. This work also focuses on the investigation of a suitable approach to choose absence locations and the influence of this choice on the predicted hazard as their influence is not comprehensively studied. We aim at enabling time-dependent and dynamic hazard mapping by incorporating time-dependent precipitation data into the training dataset with static features. Inclusion of temporal trigger factors, i.e. rainfall, enables a regularly-updated landslide hazard map based on the precipitation forecast. Our approach includes the investigation of a suitable precipitation metric for the occurrence of shallow landslides at the absence locations based on the statistical evaluation of the precipitation behavior at the presence locations. In this presentation, we will describe the modular workflow as well as the benchmark dataset and show preliminary results including above mentioned approaches to handle absence locations and time-dependent data.