Bemah Ibrahim

ORCID, self-asserted

ORCID 0000-0002-1877-3859

Publications

Citations

156

h-index

Computed over the works the graph holds, so it is a floor rather than a career figure.

i10-index

Publications

9 works in the graph, newest first.

Reliable slope stability prediction with guaranteed coverage: integrating conformal prediction and multi-method explainability
2026Smart Construction and Sustainable CitiesJournalOpen access0 citations10.1007/s44268-026-00094-w
A Transparent and Valid Framework for Rockburst Assessment: Unifying Interpretable Machine Learning and Conformal Prediction
2024Rock Mechanics and Rock EngineeringJournalFirst authorCorresponding12 citations10.1007/s00603-024-03847-0
Predicting reservoir temperature of geothermal systems in Western Anatolia, Turkey: A focus on predictive performance and explainability of machine learning models
2023GeothermicsJournalFirst author30 citations10.1016/j.geothermics.2023.102727
A new implementation of stacked generalisation approach for modelling arsenic concentration in multiple water sources
2023International Journal of Environmental Science and TechnologyJournalFirst author6 citations10.1007/s13762-023-05343-4
Modelling of arsenic concentration in multiple water sources: A comparison of different machine learning methods
2022Groundwater for Sustainable DevelopmentJournalFirst author29 citations10.1016/j.gsd.2022.100745
A novel XRF-based lithological classification in the Tarkwaian paleo placer formation using SMOTE-XGBoost
2022Journal of Geochemical ExplorationJournalFirst author25 citations10.1016/j.gexplo.2022.107147
A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines
2022Geosystems and GeoenvironmentJournalOpen access22 citations10.1016/j.geogeo.2022.100038
Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)
2022SustainabilityJournalOpen accessFirst author17 citations10.3390/su141911843
Assessing the Suitability of Boosting Machine-Learning Algorithms for Classifying Arsenic-Contaminated Waters: A Novel Model-Explainable Approach Using SHapley Additive exPlanations
2022WaterJournalOpen accessFirst author15 citations10.3390/w14213509