An approach for prospective forecasting of rock slope failure time

Rock slope failures globally account for most single-event landslide disasters. Climatic changes in mountain areas boost failure activity and the demand for reliable failure time forecasts. State-of-the-art prediction models are often confused with high-frequency slope deformation data. Prospectively, they provide ambiguous forecasts as data filtering, starting point definition and forecast uncertainty remain arbitrary. Here, we develop a prospective failure time forecast model that applies multiple filtering and inverse velocity percentiles to minimize subjective decisions. We test the concept with 14 historic slope failures of 10(2)-10(8) m(3) including 46 displacement datasets from different sensors. After automatic detection of the onset of acceleration, the failure time of all events is forecasted to within -1 & PLUSMN; 17 h for higher-frequency data and -1 & PLUSMN; 4 d for daily data with a final mean uncertainty of 1 & PLUSMN; 1 d and 7 & PLUSMN; 4 d that is estimated in real-time. This prospective approach overcomes previous long-standing problems by introducing a robust and uniform concept across various types of catastrophic slope failures and sensors. A prospective failure time forecast model which applies filtering and inverse velocity percentiles to automatically detect the onset of acceleration ahead of rock slope failures can do so with sufficient time to allow action to be taken