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| Titel: |
A hybrid ensemble model of kriging and neural network for ore grade estimation |
| Auteur: |
Dutta, S. Misra, D. Ganguli, R. Samanta, B. Bandopadhyay, S. |
| Verschenen in: |
International journal of mining, reclamation and environment |
| Paginering: | Jaargang 20 (2006) nr. 1 pagina's 33-45 |
| Jaar: |
2006-03-01 |
| Inhoud: |
This paper presents a new hybrid methodology involving kriging and artificial neural network for ore grade estimation of two variables namely, Al2O3% and SiO2%, in a bauxite deposit. The dataset was divided into three statistically similar subsets: training, calibration and validation sets using a genetic algorithm. The proposed hybrid ensemble model was formed using a kriging model and several neural network models. The outputs of these component models were combined using two methods to produce a unified prediction. The results indicated that the hybrid model was not a better estimator than the kriging model for the variable Al2O3%. However, it provides slightly better performance in comparison to any of the other component models in the ensemble for the variable SiO2%. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 1 van 7 gevonden artikelen
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