## Results
### Model Performance Evaluation  
In order to analyze and evaluate the prediction performance of the GM-PSO-LSSVM for different sampling intervals, the 100 testing subjects were fuzzy clustered by the FCM algorithm by their features (length and weight). For each group, the predicted time length was divided into intervals of 10, 20, 30, and 40 min. In live flatfish waterless transportation, the abnormal oxygen consumption might occur at any stage of delivery. The prediction model could predict abnormal activity of live fish respiration in the form of oxygen level by electronic observation, about 10 to 40 min before it occurred. It thus had enough time to take countermeasures, giving an early warning as well as performing adjustment management inside the waterless shipment containers through supplying and reducing the oxygen level when some influences might possibly have caused a large consumption of oxygen. In Figure 7 the different forecasting results with different time length by utilizing GM-PSO-LSSVM and LSSVM as well as GM (1, 1) are shown. Those intelligence forecasting methods were compared with an actual oxygen time series that was acquired in flatfish waterless transportation monitoring.  
After comparing the four-time length prediction, the average execution times are shown in Table 3. The average of the forecasting time was executed five times to obtain a reliable result by using the evaluation indices in Table 2. The hybrid method was slightly time-consuming, but it could satisfy the efficiency of requirements in early warning or in alerting businesses in live fish delivery.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & **Equation** \\ \hline MAE & \(\frac{1}{k}\sum\limits_{i=1}^{k}\left|v_{i}^{real}-v_{i}^{predict}\right|\) \\ \hline MAPE & \(\frac{1}{k}\sum\limits_{i=1}^{k}\left|\frac{v_{i}^{real}-v_{i}^{predict}}{v_{i}^ {pred}}\right|\) \\ \hline RMSE & \(\sqrt{\frac{1}{k}\sum\limits_{i=1}^{k}\left(v_{i}^{real}-v_{i}^{predict}\right) ^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The indices for prediction error (MAE, RMSE, MAPE).  
Testing data sets were evaluated in Figure 8 for the forecasting values of the oxygen concentration with different time interval. It analyzed the forecasting error indices by the error of forecasting: (a) mean absolute error (MAE); (b) mean absolute percent error (MAPE); and (c) root-mean-square error (RMSE). In terms of the result, with the increasing of time intervals were found to increase the error between the predicted value and measured value. However, the figure shows that this model can accurately predict the oxygen concentration within time intervals of 10 min. The result of the 10-min forecasting relatively meets the requirement in waterless live transportation.  
In the meanwhile, the two key stress indices (glucose, lactic acid) were collected discretely by analyzing the caudal venous blood of flatfish. The specific changes of stress during waterless live delivery are demonstrated in Table 4. At the beginning of transportation, the glucose and lactic acid were in a relative low state. With time, the glucose was kept in a stable high state. Meanwhile, lactic acid was not observed in the middle of the figure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **10 min** & **20 min** & **30 min** & **40 min** \\ \hline GM (1, 1) & 0.32 & 0.49 & 0.55 & 0.71 \\ LSSVM & 0.31 & 0.35 & 0.47 & 0.56 \\ GM-PSO-LSSVM & 0.51 & 0.74 & 0.89 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Forecasting execution time in different predicted time length (unit: second).  
Figure 7: Forecasting model comparison with different predicted time length. (a) 10 min; (b) 20 min; (c) 30 min; (c) 40 min.  
acid still showed the increasing trends. Therefore, reducing of oxygen might lead to certain stress responses in flatfish waterless transportation.