Slope displacement forecasting is crucial for geotechnical early warning. Conventional deterministic models cannot adapt easily to real-time monitoring, and large deep-learning models demand extensive data and compute. We leverage IBM’s Tiny Time Mixer (TTM), a compact, MLP-Mixer inspired time-series model with specialized temporal and channel mixing that scales linearly with sequence length. TTM was applied to daily aggregated data from six instrumented highway embankment slopes, including soil moisture, matric suction and temperature at 1.5–5 m depths, plus rainfall. These inputs yielded 16-channel time series of roughly 950–998 points per slope, while the displacement target is available only at sparse measurement times, comprising approximately 36–38 inclinometer surveys per slope over ~ 2.75 years. A transfer-learning protocol was used; the model was sequentially fine-tuned on four slopes and evaluated zero-shot on two held-out slopes. On trained slopes, TTM forecasts proved highly accurate. For example, on the active Terry Road slope the fine-tuned model gave MAE ≈ 0.394 mm (R2=0.99), and on the I-220 N Ramp MAE ≈ 0.257 mm (R2=0.97). Even on more stable slopes, absolute errors were sub-millimetric (e.g. MAE ≈ 0.005–0.023 mm, with R2=0.84 and 0.72). Crucially, in zero-shot tests the model maintained high accuracy on unseen sites: the Sowell Road slope forecast had MAE = 0.132 mm (R2=0.99) and McRaven Road MAE = 0.203 mm (R2=0.96). These results indicate that learned precipitation–displacement relationships transfer robustly across slopes. Overall, this study shows that lightweight, efficient time-series models like TTM can deliver accurate, real-time slope forecasts for early warning in resource-constrained geotechnical monitoring applications.
Mahmud et al. (Tue,) studied this question.