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 used for the different sciences because they have high
capabilities for estimating different variables. Mining engineers can
use the book to determine the ore grade accurately. This book helps
identify mineral-rich regions for exploration and exploitation.
Exploration costs can be decreased by using the models in the current
book. In this book, the author discusses the new concepts in mining
engineering, such as uncertainty in ore grade modeling. Ensemble models
are presented in this book to estimate ore grade. In the book, readers
learn how to construct advanced machine learning models for estimating
ore grade. The authors of this book present advanced and hybrid models
used to estimate ore grade instead of the classic methods such as
kriging. The current book can be used as a comprehensive handbook for
estimating ore grades. Industrial managers and modelers can use the
models of the current books. Each level of ore grade modeling is
explained in the book. In this book, advanced optimizers are presented
to train machine learning models. Therefore, the book can also be used
by modelers in other fields. The main motivation of this book is to
address previous shortcomings in the modeling process of ore grades. The
scope of this book includes mining engineering, soft computing models,
and artificial intelligence.
