Dissolved Oxygen Prediction in Prawn Ponds from a Group of One Step Predictors

Abstract In this paper we have presented a novel approach to predict dissolved oxygen in prawn ponds. It is necessary to maintain dissolved oxygen above a certain level in the ponds for expected growth and survival of the prawns. An accurate prediction of dissolved oxygen can assist farmers to take necessary measures to maintain dissolved oxygen levels ideal for prawn growth. Existing approaches to dissolved oxygen prediction performs well on short term, however incurs high error on long term prediction. We propose a new approach where a group of predictors are developed where each model predicts a certain time stamps ahead. Each predictor is trained on sampled data so that it predicts a step ahead prediction only, however, the sampling process decides on the actual number of time stamp ahead prediction. Since step ahead predictor acts like a short term predictor, it incurs small error even at higher time stamp ahead prediction. Experimental results demonstrate that the proposed approach achieves significantly lower error on long term prediction compared to other existing approaches.