Abstract Machine learning algorithms have been widely applied in mineral prospectivity
mapping (MPM). In this study, we implemented ensemble learning of extreme gradient …

This paper introduces the concept of geodata science-based mineral prospectivity mapping
(GSMPM), which is based on analyzing the spatial associations between geological …

Convolutional neural network (CNN) has demonstrated promising performance in
classification and prediction in various fields. In this study, a CNN is used for mineral …

Abstract Machine learning (ML) algorithms are widely applied in various fields owing to their
strong ability to abstract high-level features from a large number of training samples …

Electrical conductivity (EC), one of the most widely used indices for water quality
assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest …

The excellent performance of convolutional neural network (CNN) and its variants in image
classification makes it a potential perfect candidate for dealing with multi-geoinformation …

In recent times, machine learning has become increasingly prominent in predictive
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …

The traditional convolutional neural networks applied in mineral prospectivity mapping
usually extract features from only one scale at each iteration, resulting in plain features. To …

There has been in recent years a trend towards adopting deep neural networks for
addressing earth science problems. Of the various deep neural networks applied to different …

Prediction of geochemical concentration values is essential in mineral exploration as it plays
a principal role in the economic section. In this paper, four regression machine learning (ML) …