RETRACTED ARTICLE: Monitoring the quality of water in shrimp ponds and forecasting of dissolved oxygen using Fuzzy C means clustering based radial basis function neural networks

The success of shrimp farming lies in the proper monitoring of water quality in the pond. Of all the water quality parameters the dissolved oxygen plays a vital role in the proper growth of shrimps and influences mortality to a great extent. Traditional methods of sensing the water quality especially dissolved oxygen is done in laboratories far away from the actual pond site and it is often time consuming. Hence an accurate forecasting model which predicts the changes in the DO content is quintessential to decrease mortality, yield export quality shrimps and reduce operational costs. Water quality datasets are collected by using devices fabricated with wireless water quality sensors which floats in the ponds. Fuzzy C means was chosen as the clustering method based on the dataset collected and radial basis functions neural networks were constructed with accurate number of hidden neurons to predict the changes in the dissolved oxygen. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R) and Willmott index of agreement (WIA) were used as the evaluation parameters in comparison with the Multilayer Perceptron based Backpropagation Neural Network and Standard Radial Basis Function Neural Network for prediction of the DO content. The results show that the proposed method is effective with reduced errors and correlation coefficient close to unity. This accurate prediction of the dissolved oxygen helps the farmers to take corrective action when required and decrease the operational costs and produce export quality shrimps.