  This paper describes application of information granulation theory, on the
analysis of "lugeon data". In this manner, using a combining of Self Organizing
Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are
obtained. Balancing of crisp granules and sub- fuzzy granules, within non fuzzy
information (initial granulation), is rendered in open-close iteration. Using
two criteria, "simplicity of rules "and "suitable adaptive threshold error
level", stability of algorithm is guaranteed. In other part of paper, rough set
theory (RST), to approximate analysis, has been employed >.Validation of the
proposed methods, on the large data set of in-situ permeability in rock masses,
in the Shivashan dam, Iran, has been highlighted. By the implementation of the
proposed algorithm on the lugeon data set, was proved the suggested method,
relating the approximate analysis on the permeability, could be applied.
