Evaluaion of Various Methods for Weighting Spatial Information in GIS (Case Study: Mineral Potential Mapping)

Mines, as the main sources of raw materials used in the industry, have an inevitable role in today’s human life.  Exploration of new mines is an obligation for more raw materials.  New methods, technology and information have had great effects on the mine exploration industry. Due to the power and capability of Geographical Information Systems (GIS) in processing the spatial data, they can be effectively used in mine exploration.
Mines are recognized based on the preliminary data which are collected in the field.  GIS can be used in the processing, weighting, and extracting information out of the data to facilitate the mine exploration process.  What is quite important in this respect is nothing but the determination of relative weight of different data layers to combine them.  Assigning different weights to the data and/or using different methods to combine them, have a great impact on the final result.  Therefore, studying and comparing different weighting schema are objectives as well as the subject of this paper.
Weighting methods can be classified into two main groups of data-driven and knowledge-driven categories.  For each group one example is studied in this research. To be more specific, Analytical Hierarchical Process (AHP), as a knowledge-driven method, and Artificial Neural Networks as a data-driven one are studied and implemented in this research. 
These different methods are used to produce the mineral potential map as one of the last steps in the mine exploration process. The information collected from the boreholes are used to evaluate the results. Numerical experimentations showed that the artificial neural network used in this study is the most successful method. It is shown that knowledge driven-methods are very much affected by the degree of knowledge and specialization of the experts. Meanwhile, different methods of using the knowledge were resulted in different solutions.
In this paper Fig. 1 shows the process of calculation of weights in AHP method, while Fig. 2 represents a general form of an artificial neural network used in this study. Fig. 3 illustrates case study area and Fig.4 shows the stages of data preparation. Fig. 5 illustrates input maps and data such as boreholes. Fig. 6 and Fig. 7 show procedures of implementing AHP and artificial neural networks respectively. Fig. 8 represents the percent of correct predictions for implemented methods and at last, Fig. 9 illustrates two mineral potential maps produced by AHP and artificial neural networks. Finally, Tables 1 and 2 represent the weights obtained from AHP and artificial neural networks respectively.