Real-Time Forecast of Catastrophic Landslides via Dragon-King Detection

Abstract

Catastrophic landslides characterized by runaway slope failures remain difficult to predict. Here, we develop a physics-based framework to prospectively assess slope failure potential. Our method builds upon the physics of extreme events in natural systems: the extremes so-called "dragon-kings" (e.g., slope tertiary creeps prior to failure) exhibit statistically different properties than other smaller-sized events (e.g., slope secondary creeps). We develop statistical tools to detect the emergence of dragon-kings during landslide evolution, with the secondary-to-tertiary creep transition quantitatively captured. We construct a phase diagram characterizing the detectability of dragon-kings against "black-swans" and informing on whether the slope evolves toward a catastrophic or slow landslide. We test our method on synthetic and real data sets, demonstrating how it might have been used to forecast three representative historical landslides. Our method can in principle considerably reduce the number of false alarms and identify with high confidence the presence of true hazards of catastrophic landslides.