Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method

Danish Khan, Wasim Akram, Sajid Ullah

Open source

DOI: 10.1080/10106049.2025.2514725
Published: 2025-06-10
Container: Geocarto International
Publisher: Informa UK Limited
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1080/10106049.2025.2514725,
  title = {Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method},
  author = {Danish Khan and Wasim Akram and Sajid Ullah},
  year = {2025},
  journal = {Geocarto International},
  doi = {10.1080/10106049.2025.2514725},
  url = {https://doi.org/10.1080/10106049.2025.2514725}
}

RIS

TY  - JOUR
TI  - Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method
AU  - Danish Khan
AU  - Wasim Akram
AU  - Sajid Ullah
PY  - 2025
JO  - Geocarto International
DO  - 10.1080/10106049.2025.2514725
UR  - https://doi.org/10.1080/10106049.2025.2514725
ER  -

APA

Khan, D., Akram, W., & Ullah, S. (2025). Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method. Geocarto International. https://doi.org/10.1080/10106049.2025.2514725

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crossref · retrieved 2026-11-09T09:17:11.391Z