Prediction of Mining-Induced Subsidence in Saudi Arabia Phosphate Mines Using ANN Method

This study develops and validates an artificial neural network (ANN) model to predict mining-induced land subsidence in Saudi Arabia’s Al-Jalamid and Umm Wu’al phosphate mines. A multilayer perceptron is used with optimized hyperparameters based on four inputs (ground point position, distance from extraction center, accumulated exploitation volume, and time). The optimal configuration (5 hidden layers, 64 nodes, 240 epochs) achieves RMSE = 22 mm and MAE = 13 mm, outperforming traditional numerical/statistical baselines. Case-study validation at both mines confirms robustness (e.g., RMSE ≈ 20 mm, MAE ≈ 12 mm), enabling practical mitigation such as ground reinforcement and extraction-rate control. The results demonstrate that a tuned ANN provides accurate, operationally useful subsidence forecasts, supporting safer and more sustainable mine planning.

@article{GHARBI2025,
  title     = {Prediction of Mining-Induced Subsidence in Saudi Arabia Phosphate Mines Using ANN Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Atef GHARBI and Mohamed AYARI and Yamen El Touati and Zeineb Klai and Mahmoud Salaheldin Elsayed and Elsaid Md. Abdelrahim},
  doi       = {10.14569/IJACSA.2025.0160852},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160852}
}