Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data

A long-standing challenge for forecasting from big, spatiotemporal data with nonstationarity is scalability. Here, we overcome this by developing a scalable forecasting framework for slope stability and hazard dynamics and what-if-scenario analytics. The framework incorporates recent findings from granular physics and dynamics of precursory failure and delivers explainable, actionable, and timely intelligence on an impending collapse. To demonstrate its performance, we applied the framework to a slope stability radar (SSR) monitoring data from a rock slope in an operational mine. The forecast of surface motion for the entire monitoring domain is used to establish probabilistic outcomes for what-if scenarios, as well as the likely location, geometry, and timing of failure. Next, we implement a four-pronged approach that exploits SSR data from a second slope having different but similarly complex dynamics, in addition to data simulations, to test robustness to false positive and/or negative predictions and reproducibility of forecast performance. In anticipation of synergistic advances in remote sensing and analytics, this effort takes the first steps toward an augmented intelligence platform for future-focused slope stability analytics and decision-making in real time.