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Peer-reviewedCC BY 4.0Scientific Reports

Enhancing soil creep index prediction accuracy using optimized XGBoost and LightGBM models with bio-inspired optimization algorithms: SHAP- and LIME-based explanation methods

  • Rana Muhammad Adnan Ikram
  • Meysam Alizamir
  • Salim Heddam
  • Aliakbar Gholampour
  • Sungwon Kim
  • Sul-Min Yun
Open at sourceDOICite

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Cite

BibTeXRIS
BibTeX
@article{ikram2026enhancing,
  title = {{Enhancing soil creep index prediction accuracy using optimized XGBoost and LightGBM models with bio-inspired optimization algorithms: SHAP- and LIME-based explanation methods}},
  author = {Rana Muhammad Adnan Ikram and Meysam Alizamir and Salim Heddam and Aliakbar Gholampour and Sungwon Kim and Sul-Min Yun},
  journal = {Scientific Reports},
  year = {2026},
  month = nov,
  doi = {10.1038/s41598-026-78179-w},
  url = {https://www.nature.com/articles/s41598-026-78179-w},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Enhancing soil creep index prediction accuracy using optimized XGBoost and LightGBM models with bio-inspired optimization algorithms: SHAP- and LIME-based explanation methods
AU  - Rana Muhammad Adnan Ikram
AU  - Meysam Alizamir
AU  - Salim Heddam
AU  - Aliakbar Gholampour
AU  - Sungwon Kim
AU  - Sul-Min Yun
PY  - 2026
DA  - 2026/11/02
JO  - Scientific Reports
DO  - 10.1038/s41598-026-78179-w
UR  - https://www.nature.com/articles/s41598-026-78179-w
SN  - 2045-2322
ER  -