Prediction of Dissolved Oxygen Concentration for Shrimp Farming Using Quadratic Regression and Artificial Neural Network

In aquaculture, one of the most critical factors for sustaining life under the water is the dissolved oxygen (DO) since it affects not only the animal survival rate but also the growth rate. Therefore, in smart aquafarming, the DO content should be monitored thoroughly. As a consequence, in practice, many DO sensors are installed in systems, and they contribute markedly to the system cost. This work aims to reduce the cost by replacing some DO sensors with a model that can describe the dynamics of DO content in a specific controlled environment. Thus, we propose two predictive models: one based on the quadratic regression and another based on an artificial neural network. Experimental results show that, under the limitation of the number of data used in the model construction, both models perform equally. Also, both prediction fitted more to observed data when the DO level is low. This finding supports the practical model usage since in practice we more concern with the efficiency of the model in the case of low DO concentration.