Mine water hazards threaten safety; burst-point inflow can spike rapidly. Inflow series are nonlinear, uncertain, and often small-sample. Shi et al. (2024), Scientific Reports, propose LSTM-Transformer. Summary below.

Scenario and goals

  • Data: 245 records (2012–2022) from Baotailong mine, Heilongjiang (as in the paper).
  • Goal: rolling inflow forecasts for warning and response.
  • Challenge: overfitting; balance generalization and accuracy.

Method highlights

1. Preprocessing

ADF stationarity, EMA, Ljung-Box tests; min–max scaling; train/test ratios including 7:3 where LSTM-Transformer performed best in the study.

2. LSTM-Transformer

LSTM for long dependencies; Transformer self-attention for global structure; dense head for next-step prediction.

3. Hyperparameters

Random search + Bayesian optimization with L2 regularization and Adam.

4. Baselines

Compared with CNN, LSTM, Transformer, CNN–LSTM; the paper reports LSTM-Transformer leading on RMSE, MAE, and R² (e.g. ~18.2% / 21.3% MAE/RMSE reduction vs CNN—see original tables).

Practical takeaways

  • Treat split ratio as a first-class experiment axis on small samples.
  • Fusion can combine local dynamics and global patterns.
  • Wire forecasts into flood warning workflows.
  • Authors note limited dataset scale for generalization.

Reference

Shi, J.; Wang, S.; Qu, P.; Shao, J. Time Series Prediction Model Using LSTM-Transformer Neural Network for Mine Water Inflow. Scientific Reports 2024, 14, 18284.