Fish survival prediction in an aquatic environment using random forest model

In the real world, it is very difficult for fish farmers to select perfect species aquaculture in a specific aquatic environment. The main goal of this research build machine learning that can predict an paper, we have utilized model using random forest (RF). To validate model, used dataset environment 11 different fishes. species, characteristics including pH, temperature, and turbidity. As performance metrics, measured accuracy, true positive (TP) rate, kappa statistics. Experimental results demonstrate proposed RF-based prediction shows accuracy 88.48%, statistic 87.11% TP rate 88.5% tested dataset. addition, compare with state-of-art models J48, RF, k-nearest neighbor (k-NN), classification regression trees (CART). outperforms than existing by exhibiting higher score,