Abstract
Machine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. In this study, the relationship between changes in the physico-chemical characteristics of water and feed consumption was evaluated using machine learning methods. Eleven physico-chemical characteristics (temperature, pH, dissolved oxygen, electrical conductivity, salinity, Nitrite nitrogen, nitrate nitrogen, ammonium nitrogen, total phosphorus, total suspended solids, and biological oxygen demand) of water were evaluated in terms of fish feed consumption by using ML methods. Among all the measured physico-chemical characteristics of water, temperature was determined to be the most important parameter to be evaluated in fish feeding. Moreover, pH2, eC2, TP2, TSS2, S2 and NO2 parameters detected in the outlet water are more important than those detected in the inlet water in terms of feed consumption. In the regression analysis carried out using ML techniques, the models developed with RF, GBM and XGBoost algorithms yielded better results.
Published Version
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