## 4 Discussion  
With respect to waterless live sea fish transportation, the deployment of an on-line oxygen forecasting system for improvement of survival rates has hardly been reported in current research. It represents a novel model and form of system implementation for the improvement of such live \begin{table}
\begin{tabular}{c c c c c} \hline
**Stress Type** & **0 h** & **8 h** & **16 h** & **24 h** & **32 h** \\ \hline Glucose (mmol/L) & 1.25 \(\pm\) 0.2 & 1.86 \(\pm\) 0.3 & 5.83 \(\pm\) 0.1 & 5.72 \(\pm\) 0.1 & 5.78 \(\pm\) 0.2 \\ Lactic acid (mmol/L) & 1.4 \(\pm\) 0.3 & 1.6 \(\pm\) 0.2 & 2.7 \(\pm\) 0.1 & 3.2 \(\pm\) 0.2 & 3.9 \(\pm\) 0.1 \\ \hline \end{tabular} Transport duration of all trials was 32 h (\(p<\) 0.05). The sample points are 0, 8, 16, 24, 32 h.  
\end{table} 
Table 4: Changes of key stress factors during transportation. Figure 8: The error of the forecasting (**a**) mean absolute error (MAE); (**b**) mean absolute percent error (MAPE); (**c**) root-mean-square error (RMSE). delivery. Therefore, it very critically important to accurately obtain information on forthcoming oxygen levels to sustain flatfish survival quality. The standard LSSVM and the GM (1, 1) methods have been widely used in the prediction field. However, they have numerous drawbacks, like low generalization and poor stability. Moreover, when executing forecast operations they do not fully take the forthcoming ambient factors into account. These drawbacks in a prediction model is not accurate enough. To analyze the forecasting capacity of the hybrid model based on GM-PSO-LSSVM, the standard LSSVM and the grey model were selected for comparison with four forecasting lengths from 10 to 40 min. Ten-minute forecasting has been proven more accurate than oxygen prediction of 20 min or more. In this paper, the present methods are used in problems of relatively poor stability and low generalization to improve the forecast accuracy of on-line oxygen consumption. After grouping of testing, it was found that it is more promising to utilize the combined model GM-PSO-LSSVM for on-line forecasting operations. In Figure 7, the four intervals (10, 20, 30, 40 min) for different prediction methods were used for testing. It shows that the proposed GM-PSO-LSSVM model has a stronger learning capacity than the GM (1, 1) method and standard LSSVM method for small samples, and it also achieves excellent generalization. The presented model provides more accurate predictions of the short time period of oxygen forecasting strategy compared with the LSSVM and GM (1, 1) methods. Thus, the GM-PSO-LSSVM can guarantee improvements in stability and accuracy, and it is a suitable and effective method for predicting the oxygen level during live flatfish waterless transportation. It can satisfy the logistical needs of early warning and the corresponding management relatively well. The indexes MAE, MAPE, and RMSE were analyzed to study the forecasting capacity of the three models shown in Figure 8. To represent the error trend, the most recent sampled data with an interval of forecasting results were used to analyze the forecasting performance of the above models. The performance indexes were calculated and demonstrated the trend of error. As for the GM-PSO-LSSVM model in the MAE index, the forecasting accuracy has been improved on average by 20.5% and 52.3% compared with the LSSVM and GM (1, 1) methods respectively. With the situation of index MAPE, the forecasting accuracy of the hybrid model shows an improvement of 21.9% and 54.2% (on average) compared to LSSVM and GM, respectively. In the RMSE index, the forecasting error of the GM-PSO-LSSVM method has been reduced 21.4% and 51.7% compared with other methods, respectively. The accuracy of calculation is not good enough for decision-making due to the forthcoming ambient changes that may lead to instability and variation when the forecasting time is been lengthened.