To overcome this issue, we developed a novel machine learning scheme that investigates the environmental (hydrological and geological) characteristics of mass movements and the connectivity information of formal settlements and road network data in terms of graph structures. In particular, we study the interaction of the probabilistic mass movement susceptibility (derived from the environmental properties by means of a supervised ensemble graph neural network) on the graph representing the road network connecting the formal settlements. As a result, we derive for each formal settlement a probability of being indirectly affected by mass movements (e.g., the probability to be isolated as a result of mass movements affecting their surroundings) by graph spectral clustering.
We tested this architecture (named Intergraph) on the Norwegian territory, taking advantage of over 68,000 incidents of reported mass movements since 1957. Our approach achieved an overall performance of 86.25% with the 2020 Gjerdrum quick clay incident as a demonstrated case study. With the intensifying effects of climate change, our study has opened an opportunity to develop solutions for adaptation and mitigation through a new holistic graphical perspective to assess various large-scale geospatial datasets of risk elements such as exposure, vulnerability, and hazard.