Due to presence of inherent uncertainties in geoscience data caused by various unknown and even known geological phenomenon, applying simple boolean logics to infer from such data would eventually lead to significant estimation errors. One way out of this difficulty is to employ knowledge based methods such as Fuzzy logic inference models which handles such uncertainties through considering gradual nature of properties of qualitative parameters under investigation. Fuzzy logic models are considered as knowledge based techniques and when they exploit the advantages of data driven techniques such as neural network form a very powerful Finally the above thematic data were integrated using both neural network and neurofuzzy algorithms to predict the favorable metallic occurrences. Four training sites were adopted for both techniques and based on the obtained predicting models the entire were processed for favorability. The final results for neurofuzzy prediction show that 95.2 percent of the known copper deposits were granted as favorable locations compared to the 76.1 percent for that of neural network prediction. This figure for known iron deposits was reduced to 72.2 for neurofuzzy method compared to that of 54.5 for neural network while for lead and zinc deposits both methods gave the same results covering 62.5 percent of known deposits.
