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 102-108 m3 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 ± 17 h for higher-frequency data and −1 ± 4 d for daily data with a final mean uncertainty of 1 ± 1 d and 7 ± 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.