A0484
Title: Landslide forecast by time series modelling and analytics of high-dimensional and non-stationary ground motion data
Authors: Guoqi Qian - The University of Melbourne (Australia) [presenting]
Antoinette Tordesillas - The University of Melbourne (Austria)
Hangfei Zheng - The University of Melbourne (Australia)
Abstract: High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events. For timely and reliable forecasts from them, we developed a new statistical approach based on error-correction cointegration (ECC), vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC-VAR-EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5000+ time states and several thousand locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC-VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC-VAR-EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance.
