الكلمات المفتاحية
Fuzzy inferencing unit
Matlab
How to Cite
الملخص
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.
References
Al-Zahaby, A.S., El-drawany, M.A., Mahmoud, H.H., and Abdalla, M.A.F., (2018). Some biological aspects and population dynamics of the gilthead sea bream from Bardawil lagoon, Sinai, Egypt, Egyptian Journal of Aquatic Biology & Fisheries, 22: 295-308.
Cheung, W.W., Pitcher, T.J. and Pauly, D., (2005). A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing, Biological conservation, 124: 97-111.
Cheung, W.W., Pitcher, T.J., and Pauly, D., (2007). Using an expert system to evaluate vulnerabilities and conservation risk of marine fishes from fishing, New research on expert system, Nova Science Publishers, New York.
Cooper, A.B., (2006). A guide to fisheries stock assessment: from data to recommendations, University of New Hampshire, Sea Grant College Program.
Froese, R., and Pauly, D., (2020). FishBase. World Wide Web electronic publication. www.fishbase.org, version (08/2021).
Hamwi, N., (2011). Age Growth rate and Reproduction Biology of Bogue (Boops boops L.) at Syrian coast, Journal of Al-Baath University, 34: 99-124.
Hamwi, N., (2012). Estimation of Survival, Mortality and Exploitation rates of Bogue (Boops boops L.) at Syrian coast, Journal of Al-Baath University, 34: 253-274.
Hamwi, N., (2017). Growth biology and Mortality, Survival and Exploitation rates of Oblada melanura (Sparidae) at Syrian coast, Journal of Al-Baath University, 39: 11-34.
Hamwi, N., and Ali-Basha, N., (2021). Growth biology of the Red Sea Goatfish Parupeneus forsskali from the Syrian Coast (Eastern Mediterranean Sea), The Arab Journal for Arid Environments, 16 (2) (In print).
Hamwi, N., and Ali-Basha, N., (2019). Estimation of the vulnerability of some Sparidae species to fishing in the Eastern Mediterranean Sea (Syrian coast) by fuzzy logic method, Journal of Al-Baath University, 41: 129-160.
Jones, M.C., and Cheung, W.W.L., (2017). Using fuzzy logic to determine the vulnerability of marine species to climate change, Glob Change Biol.,1–13. https://doi.org/10.1111/gcb.13869.
Mahmoud, H.H., (2010). Age, growth and mortality of saddled bream, Oblada melanura (Linnaeus, 1758) in Abu Qir Bay, Egypt, Egyptian Journal of Aquatic Research, 36(2): 317-322.
Sivanandam, S.N., Sumathi, S., and Deepa, S.N., (2007). Introduction to fuzzy logic using MATLAB, Berlin: Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35781-0.
The Math Works, Inc. MATLAB. Version 2020a, The Math Works, Inc. 2020 Computer Software. https://www.mathworks.com/.
Zadeh, L.A., (1996). Knowledge representation in fuzzy logic, Fuzzy Sets and Systems, 764-774.
Zadeh, L.A., (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy sets and systems, 90: 111-127.