Abstarct: Uncertainty with reality is mixed with reality, and accurate presentation of reality is not always possible. Modeling and estimating the development of mineralization due to the presence of ambiguities in exploration data is also not excluded from this principle, and avoidance of uncertainty in the exploration data modeling is usually difficult and its approximate presentation in decision making The final is a must-see. In this research, an optimal method, derived from the integration of knowledge-baxsed and data-baxsed methods of the theory of estimating functions (Demester-Shafer), was presented. For this purpose, an artificial data series was first produced and each of the data-baxsed and knowledge-baxsed methods using the data was investigated and a method for integrating the data-baxsed and knowledge-baxsed theory of estimating functions was proposed. These methods have been investigated with real data that have been obtained from the Central Iron Mineral Range of Iran, and their effectiveness has been investigated. Finally, the results of different methods were compared. The best results are shown by the combined method of artificial neural network and FAHP (Fuzzy-AHP) with available data. In this report, first in the first chapter, we describe the generalities of the research; in chapter two, the theoretical principles of the Dempse-Straße method are stated. In the third and fourth chapters, using artificial and real data, the research has been followed. Finally, in the fifth chapter, the results and suggestions of the research are presented.