Weighting Spatial Information in GIS for Copper Mining Exploration
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Copyright: © 2020 Farhad Hosseinali and Ali Asghar Alesheikh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Exploration of new mines is vitally important for human life. Geospatial Information Systems (GIS) can be effectively used in the gathering, weighting, analyzing and presenting spatial and attribute information to facilitate the mine exploration process. The success of mine exploration largely depends on: the identification of governing factors, the determination of their impacts and the selection of suitable models to integrate the parameters. Weighting methods are classified into two main groups: data-driven and knowledge-driven. Six weighting methods are identified and scientifically assessed in this study, namely; Ratio Estimation, Analytical Hierarchy Process (AHP), Delphi, Weight of Evidence, Logistic Regression and Artificial Neural Networks (ANN). The first three are examples of knowledge-driven and the last three are classified in the data-driven group. In order to evaluate the methods, the information of 26 copper boreholes are used. Numerical experimentations showed that the artificial neural network used in this study is the most accurate method because it could predict the characteristics of all boreholes correctly. It is shown that knowledge-driven methods are very much affected by the degree of knowledge and the specialization of experts. The results indicated that AHP is the most successful method among knowledge-driven class and could predict the characteristics of 82% of boreholes correctly.
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- mineral potential map
- artificial neural networks