Abstract:

In order to improve the multi-parameter prediction accuracy of dissolved oxygen, an ISSA-LSTM (Improved Sparrow Search Algorithm-Long and Short-Term Memory Neural Networks) dissolved oxygen prediction model is developed based on the ISSA and LSTM. The model is applied to the prediction of dissolved oxygen in eel breeding ponds at Shanghai academy of agricultural sciences. The sparrow search algorithm is optimized using chaos mapping, lensing imaging backward learning, adaptive adjustment and Cauchy variation. The data are pre-processed by wavelet transform, the input parameters for model training are determined using principal component analysis. The training results show that the correlation coefficient, root mean square error, mean square error and mean absolute error are 0.911, 1.392 mg·L^-1, 1.938 mg·L^-1 and 0.992 mg·L^-1, which are all better than those in the control model. The choice of model input parameters also have an impact on the model prediction results, with the best model predictions using both moderately and strongly correlated parameters with dissolved oxygen as input parameters. The training results provide a new perspective for the development of the dissolved oxygen multi-parameter prediction model.

Key words: dissolved oxygen prediction, long and short-term memory neural networks, sparrow search algorithm, principal component analysis, wavelet transform, Cauchy variation, chaos mapping, eel farming