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 a 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.

A new version of stochastic complexity for a parametric statistical model is derived, based on a class of two-part codes. We show that choosing the quantization in the first step according to the Fisher information is optimal and we compare our approach to a recent result of Rissanen (1983). An application to robust regression model selection is presented.

The concept of stochastic complexity developed by Rissanen(1989) leads to consistent probability density estimators. These density estimators are defined to achieve the best compromise between likelihood and simplicity, namely, the stochastic complexity based on the observed sample. In this paper, a density estimation-based complexity decision rule is proposed which uses the quality of these estimators to estimate the corresponding unknown element of the true probability density. In the development, we introduce a loss function which includes the total variation of the squared distance of the characteristic functions to evaluate the performance of the density decision rule. The resulting complexity density decision procedure is shown to be admissible, to achieve the minimum expected risk, and to form a minimal complete class.>