(untitled) (Journal of Hama University, 2022)
When studying the growth of fishery populations, the question of validation of growth ‎parameter estimates often arises due ‎to the lack of reliability of some of the methods used in ‎obtaining such estimates. It has been suggested a model, ‎which aims to find an advanced ‎scientific model to estimate the growth of the fishery populations by artificial ‎intelligence ‎technologies such as fuzzy logic and thus determine the ability to make decisions regarding ‎this ‎growth. An expert system has been built that contains inference rules which consist of ‎four input variables (VBGF parameter, K; age at recruitment, Tr; ‎natural mortality rate, M; ‎exploitation,‎‏ ‏E) for ‎each species. The research has highlighted to important results: ‎ ‎- The proposed model depends on a strong inference system in which all cases ‎studied for ‎four input ‎variables were discussed and It could be applied to all fish species, ‎which helps to ‎increase ‎opportunities to ‎improve fishing management and sustainability.‎ ‎- A high degree of reliability of the model to estimate the growth of the fishery populations ‎compared to the Musick criterion, which is based only on the value of ‎the growth factor (K) ‎‎for Bertalanffy in assessing fisheries productivity.