Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis
- DOI
- 10.1038/s41598-026-57764-z
- Published
- 2026-06-12
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-57764-z,
title = {Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis},
author = {Muhammad Suliman and Maaz Khan and Touqeer Ali Rind and Farhan Ullah and Sabila Khan and Zafar Mahmood},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-57764-z},
url = {https://doi.org/10.1038/s41598-026-57764-z}
}RIS
TY - JOUR TI - Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis AU - Muhammad Suliman AU - Maaz Khan AU - Touqeer Ali Rind AU - Farhan Ullah AU - Sabila Khan AU - Zafar Mahmood PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-57764-z UR - https://doi.org/10.1038/s41598-026-57764-z ER -
APA
Suliman, M., Khan, M., Rind, T. A., Ullah, F., Khan, S., & Mahmood, Z. (2026). Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis. Scientific Reports. https://doi.org/10.1038/s41598-026-57764-z
Source records
- crossref · retrieved 2026-11-09T14:31:00.700Z