In the smart mariculture, the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously. However, the openness of the mariculture environment makes the variation of water quality nonlinear, dynamic and complex. Traditional methods face challenges in prediction accuracy and generalization performance. To address these problems, an accurate water quality prediction scheme is proposed for pH, water temperature and dissolved oxygen. First, we construct a new huge raw data set collected in time series consisting of 23,204 groups of data. Then, the water quality parameters are preprocessed for data cleaning successively through threshold processing, mean proximity method, wavelet filter, and improved smoothing method. Next, the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method. Meanwhile, the data for training is weighted by the discovered correlation coefficients. Finally, by adding a backward SRU node to the training sequence, which can be integrated into the future context information, the deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) learning network is proposed. After training, the prediction model can be obtained. The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) with similar or less time computing complexity. In our experiments, the proposed method takes 12.5ms to predict data on average, and the prediction accuracy can reach 94.42% in the next 3~8 days.

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

In smart mariculture, an automatic and accurate prediction of key water quality parameters is a significant and challenge issue. This paper focuses on the prediction of pH and water temperature parameters in key water quality parameters. Firstly, the water quality parameters are preprocessed by improved method. Then, the Pearson correlation coefficient method is used to find the correlation between the water quality parameters. Finally, the SRU (Simple Recurrent Unit) deep learning model is used to establish a prediction model for the key water quality parameters, so as to achieve accurate prediction. Meanwhile, we also evaluate the prediction effect of prediction model built by RNN (Recurrent Neural Network) deep learning network. The experimental results show that the proposed prediction method has higher prediction accuracy than the method based on RNN when the time complexity is similar. The proposed method takes 11.3ms to predict every data in average, and the prediction accuracy can reach 98.91%.

Automatic and Accurate Prediction of Key Water Quality Parameters Based on SRU Deep Learning in Mariculture

The prediction of dissolved oxygen(DO)level is complicated in aquaculture ponds as a complex system with multi-variables,nonlinearity and long-time lag.In this study,GA-LM,a hybrid neural network model combining Levenberg Marquardt(LM)algorithm and Genetic Algorithm(GA)was developed for DO level predicting in an aquaculture pond at Dalian,China.The The comparison of performance of GA-LM with the conventional Back -Propagation(BP)algorithm revealed that the predicted DO values using GA-LM model are in good agreement with the measured data,indicating that the model is capable of predicting DO accurately and rapidly.

Miao Xin-ying

Papers

A prediction model for dissolved oxygen level in a fish pond based on combination of neural network and genetic algorithm