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Related Experiment Video

Updated: Apr 16, 2026

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Slope displacement forecasting with limited field data using time series model.

Iftakhar Al Mahmud1, Yifei Li2, A Q M Zohuruzzaman2

  • 1Department of Electrical & Computer Engineering and Computer Science, Jackson State University, Jackson, MS, 39217-0168, USA.

Scientific Reports
|April 14, 2026
PubMed
Summary

Tiny Time Mixer (TTM), a lightweight deep learning model, accurately forecasts slope displacement using geotechnical data. This efficient approach enables reliable early warning systems even with limited data and computational resources.

Keywords:
TTMgeotechnical monitoringslope stabilitytime series forecastingtransfer learning

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Area of Science:

  • Geotechnical Engineering
  • Machine Learning
  • Time Series Analysis

Background:

  • Slope displacement forecasting is critical for geotechnical early warning systems.
  • Conventional models struggle with real-time monitoring, while large deep learning models require extensive data and computation.
  • There is a need for efficient, accurate, and data-light models for slope stability prediction.

Purpose of the Study:

  • To evaluate the efficacy of IBM's Tiny Time Mixer (TTM) for accurate slope displacement forecasting.
  • To assess the model's performance in resource-constrained geotechnical monitoring applications.
  • To demonstrate the transferability of learned models across different highway embankment slopes.

Main Methods:

  • Applied the compact Tiny Time Mixer (TTM) model to daily aggregated geotechnical data (soil moisture, matric suction, temperature, rainfall) from six instrumented highway embankment slopes.
  • Utilized a transfer-learning protocol, sequentially fine-tuning the model on four slopes and evaluating it zero-shot on two unseen slopes.
  • Input data comprised 16-channel time series, while displacement targets were derived from sparse inclinometer surveys over approximately 2.75 years.

Main Results:

  • TTM achieved high accuracy on trained slopes, with Mean Absolute Errors (MAE) as low as ~0.257 mm (R²=0.97) and ~0.394 mm (R²=0.99).
  • Even on more stable slopes, absolute errors remained sub-millimetric (MAE ~0.005-0.023 mm).
  • Crucially, zero-shot evaluations on unseen slopes demonstrated robust performance, with MAE of 0.132 mm (R²=0.99) and 0.203 mm (R²=0.96), indicating strong transferability of learned precipitation-displacement relationships.

Conclusions:

  • Lightweight, efficient time-series models like TTM can provide accurate, real-time slope displacement forecasts.
  • The study validates the robust transferability of TTM's learned geotechnical-environmental relationships across diverse slope sites.
  • TTM offers a viable solution for early warning systems in geotechnical monitoring, particularly in environments with limited data and computational capacity.