Abstract
Forecasting landslide failure time is important for understanding the kinematics of landslides. In addition to the famous inverse velocity method (INV), other methods such as the minimum inverse velocity method (MINV), slope gradient (SLO), and velocity over acceleration (VOA) have been proposed. Despite the importance of adopting a forecasting technique, very limited insights regarding the mentioned methods have been published. This study aims to address this gap by conducting a comparative examination of INV, MINV, SLO, and VOA on a comprehensive database of 75 historical landslides. This access to a large volume of historical data enabled us to assess the advantages and limitations of each method. Results show that MINV is the only method with better performance than INV, by 23% on average and, in 32% of cases, by at least 100%. On the other hand, the accuracy of SLO forecasts is on average 87% worse than INV forecasts. VOA results have a 6.84 to 11.68 times higher error compared to INV. VOA is highly sensitive, even more so than INV, to scatter in measurements, but the positive effect of filtration is offset by data artifacts caused by the data filtration technique. SLO, owing to its mathematical configuration, is rather insensitive to measurement scatter. Finally, the failure velocities, as calibrated by MINV, are presented for each failure mode; this can enhance the detection of the onset of acceleration and be used toward more adaptive warning thresholds in monitoring systems depending on the site setting.
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This research was completed through the (Canadian) Railway Ground Hazard Research Program, which is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC; ALLRP 549684-19), Canadian National Railway, Canadian Pacific Railway, and Transport Canada. Additionally, synthetic-aperture radar interferometry data for sets 69 and 70 were kindly provided by TRE Altamira.
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Sharifi, S., Macciotta, R. & Hendry, M.T. Critical assessment of landslide failure forecasting methods with case histories: a comparative study of INV, MINV, SLO, and VOA. Landslides (2024). https://doi.org/10.1007/s10346-024-02237-5
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DOI: https://doi.org/10.1007/s10346-024-02237-5