Enhancing assessment of direct and indirect exposure of settlement-transportation systems to mass movements by intergraph representation learning

Abstract

Amidst the intensifying extreme rainfall patterns due to climate change, global early warning systems for mass movements (e.g. landslides, avalanches) need to provide not only the coarsely aggregated danger reports, but also the necessary fine details to understand its potential implications on critical infrastructures such as transportation systems. In this study, we introduce a novel ‘intergraph’ method that enhances the exposure information using a graph-based machine learning implementation on the hydrological and geological characteristics of mass movements and the underlying connectivity of settlement-transportation systems. Demonstrating the entire country of Norway and the 2020 Gjerdrum quick clay incident as a case study, we integrated the assessment of both direct and indirect exposure information of settlement-transportation systems and their daily 1 km-by-1 km susceptibility map, which were derived from the 68 934 mass movement incidents since 1957 and the connectivity information of 4778 settlements and 257 000 km road networks. Our findings achieved 86.25% accuracy, providing a distribution of improved susceptibility estimates and identifying critical settlements in near-real-time. By interacting the graphical representations of the shared causal drivers of susceptibility and the settlement-transportation system connectivity, our study extends our understanding of the exposure of multiple interacting settlements with a high granularity degree in a unified approach.

Keywords

• mass movements
• exposure
• susceptibility
• settlement
• road network
• graph-based machine learning