Estimating Ore Grade Using Evolutionary Machine Learning Models
This book examines the abilities of new machine learning models for predicting ore grade in mining engineering. A variety of case studies are examined in this book. A motivation for preparing this book was the absence of robust models for estimating ore grade. Models of current books can also be use...
| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Singapore
Springer Nature Singapore
2023, 2023
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| Edition: | 1st ed. 2023 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Explains the importance of ore grade estimation
- Reviews machine learning models for ore grade estimation
- Explains the structure of different kinds of machine learning models
- Explains different training algorithms and optimization algorithms. This chapter also explains the structure of evolutionary machine learning models
- Explains the Bayesian model averaging and multilayer perceptron networks for estimating AL2O3 grade in a mine
- Explains the structure of inclusive multiple models and optimized radial basis function neural networks for estimating Sio2 grade in a mine
- Explains the application of hybrid kriging and extreme learning machine models for estimating copper ore grade in a mine
- Explains the application of optimized group machine data handling, support vector machines, and Adaptive neuro-fuzzy interface systems for estimating iron ore grade in mines
- Presents the conclusion, general comments, and suggestions for the next books