Isaac Ahenkorah

ORCID, self-asserted

ORCID 0000-0001-6044-1412

Publications

Citations

159

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.

A Transparent and Valid Framework for Rockburst Assessment: Unifying Interpretable Machine Learning and Conformal Prediction
2024Rock Mechanics and Rock EngineeringJournalSenior authorCorresponding12 citations10.1007/s00603-024-03847-0
Hydrogeological risk assessment for mineral exploration in Ghana: A brief overview
2024Scientific AfricanJournalOpen accessSenior authorCorresponding3 citations10.1016/j.sciaf.2024.e02218
Predicting reservoir temperature of geothermal systems in Western Anatolia, Turkey: A focus on predictive performance and explainability of machine learning models
2023GeothermicsJournalSenior authorCorresponding30 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 TechnologyJournalSenior authorCorresponding6 citations10.1007/s13762-023-05343-4
Modelling of arsenic concentration in multiple water sources: A comparison of different machine learning methods
2022Groundwater for Sustainable DevelopmentJournalCorresponding29 citations10.1016/j.gsd.2022.100745
A novel XRF-based lithological classification in the Tarkwaian paleo placer formation using SMOTE-XGBoost
2022Journal of Geochemical ExplorationJournalCorresponding25 citations10.1016/j.gexplo.2022.107147
A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines
2022Geosystems and GeoenvironmentJournalOpen accessSenior authorCorresponding22 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 accessCorresponding17 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 accessSenior authorCorresponding15 citations10.3390/w14213509