Fish survival prediction in an aquatic environment using random forest model

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