Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa

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dc.contributor.authorGawusu, Sidique
dc.contributor.authorMvile, Benatus Norbert
dc.contributor.authorAbu, Mahamuda
dc.contributor.authorKalimenze, John Desderius
dc.date.accessioned2024-09-09T10:13:08Z
dc.date.available2024-09-09T10:13:08Z
dc.date.issued2024
dc.descriptionAbstract. Full-text available at https://doi.org/10.1016/j.oreoa.2024.100065
dc.description.abstractSoil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.
dc.identifier.citationGawusu, S., Mvile, B. N., Abu, M., & Kalimenze, J. D. (2024). Machine learning based prospect targeting: a case of gold occurrence in central parts of Tanzania, East Africa. Ore and Energy Resource Geology, 100065.
dc.identifier.doi10.1016/j.oreoa.2024.100065
dc.identifier.otherDOI: 10.1016/j.oreoa.2024.100065
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4938
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofOre and Energy Resource Geology
dc.subjectMachine learning
dc.subjectGold prospect
dc.subjectCentral Tanzania
dc.subjectEast Africa
dc.subjectPredictive modeling
dc.titleMachine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa
dc.typeArticle
oaire.citation.volume17
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