Mr Bemah Ibrahim

Mr Bemah Ibrahim

Higher Degree by Research Candidate

School of Civil Engineering and Construction

College of Engineering and Information Technology

Year Citation
2024 Ibrahim, B., Tetteh Asare, A., & Ahenkorah, I. (2024). A transparent and valid framework for rockburst assessment: unifying Interpretable machine learning and conformal prediction. Rock Mechanics and Rock Engineering, 57(8), 6211-6225.
DOI Scopus5 WoS5
2024 Ibrahim, B., & Ahenkorah, I. (2024). Classifying rockburst with confidence: a novel conformal prediction approach. International Journal of Mining Science and Technology, 34(1), 51-64.
DOI Scopus16 WoS14
2023 Ibrahim, B., Ewusi, A., Ziggah, Y. Y., & Ahenkorah, I. (2023). A new implementation of stacked generalisation approach for modelling arsenic concentration in multiple water sources. International Journal of Environmental Science and Technology, 21(5), 5035-5052.
DOI Scopus4 WoS4
2023 Ibrahim, B., Konduah, J. O., & Ahenkorah, I. (2023). Predicting reservoir temperature of geothermal systems in Western Anatolia, Turkey: a focus on predictive performance and explainability of machine learning models. Geothermics, 112(102727), 1-14.
DOI Scopus20 WoS18
2022 Majeed, F., Ziggah, Y. Y., Kusi Manu, C., Ibrahim, B., & Ahenkorah, I. (2022). A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines. Geosystems and Geoenvironment, 1(2, 100038), 1-16.
DOI Scopus19
2022 Ibrahim, B., Majeed, F., Ewusi, A., & Ahenkorah, I. (2022). Residual geochemical gold grade prediction using extreme gradient boosting. Environmental Challenges, 6(article no. 100421), 1-9.
DOI Scopus22
2022 Ibrahim, B., Ewusi, A., Ahenkorah, I., & Ziggah, Y. Y. (2022). Modelling of arsenic concentration in multiple water sources: a comparison of different machine learning methods. Groundwater for Sustainable Development, 17(100745), 1-14.
DOI Scopus23 WoS21
2022 Ibrahim, B., Ewusi, A., & Ahenkorah, I. (2022). Assessing the suitability of boosting machine-learning algorithms for classifying arsenic-contaminated waters: a novel model-explainable approach using SHapley Additive exPlanations. Water, 14(21), 1-23.
DOI Scopus13 WoS12
2022 Ibrahim, B., Ahenkorah, I., Ewusi, A., & Majeed, F. (2022). A novel XRF-based lithological classification in the Tarkwaian paleo placer formation using SMOTE-XGBoost. Journal of Geochemical Exploration, 245(article no. 107147), 1-14.
DOI Scopus19 WoS16
2022 Ibrahim, B., Ahenkorah, I., & Ewusi, A. (2022). Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP). Sustainability Switzerland, 14(19), 11843.
DOI Scopus13

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